Program

Explore the accepted sessions for The Learning Ideas Conference 2026 below!

Our program will also include a featured panel discussion and keynote talks from:

  • Dr. Margaret Korosec, Director of Digital Education and Learning Innovation, University of Leeds

  • Dr. Maciej Pankiewicz, Senior Research Investigator and Associate Director at the Penn Center for Learning Analytics, University of Pennsylvania

  • Dr. Candace Thille, Associate Professor and Faculty Director for Adult and Workforce Learning at the Stanford Accelerator for Learning, Stanford University

  • Megan Torrance, CEO of TorranceLearning

The complete conference program, including session times, will be published in April.

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Emerging Digital and AI Skills in Museum Education: A Comparative Study of Three European Contexts

Luca Contardi, University of Rome Tor Vergata, Emilia Romagna, Italy

This paper examines how emerging digital technologies—including artificial intelligence, data-driven tools, and immersive media—are reshaping the skillsets required of museum professionals, and how university programs in Italy, the Netherlands, and the United Kingdom are responding to these evolving demands. Drawing on a comparative analysis of educational offerings and national approaches, the study highlights the differentiated ways in which academic institutions integrate digital competencies into museum studies curricula, reflecting broader technological and institutional priorities across Europe. The analysis stems from a broader investigation into the digital transformation of the museum sector, where professionals are increasingly expected to combine curatorial and educational expertise with advanced digital proficiencies such as digital documentation, virtual exhibition design, AI-enhanced content management, and audience engagement through interactive platforms…

Keywords: Digital Skills, Museum Education, Artificial Intelligence, Higher Education, Comparative Study

Emerging Digital and AI Skills in Museum Education: A Comparative Study of Three European Contexts

Luca Contardi

This paper examines how emerging digital technologies—including artificial intelligence, data-driven tools, and immersive media—are reshaping the skillsets required of museum professionals, and how university programs in Italy, the Netherlands, and the United Kingdom are responding to these evolving demands. Drawing on a comparative analysis of educational offerings and national approaches, the study highlights the differentiated ways in which academic institutions integrate digital competencies into museum studies curricula, reflecting broader technological and institutional priorities across Europe. The analysis stems from a broader investigation into the digital transformation of the museum sector, where professionals are increasingly expected to combine curatorial and educational expertise with advanced digital proficiencies such as digital documentation, virtual exhibition design, AI-enhanced content management, and audience engagement through interactive platforms.

The three case studies reveal distinct trajectories: UK programs tend to lead in embedding innovation and experimentation with technologies such as AI-assisted interpretation and digital storytelling; Dutch initiatives emphasize cross-sector collaboration and technological prototyping linked to museum innovation labs; while Italian curricula increasingly address sectoral needs by strengthening digital archiving, communication strategies, and the integration of digital education tools that also support inclusion and well-being.

By comparing these national models, the paper identifies strengths, gaps, and opportunities for aligning higher education with the rapidly evolving digital landscape of the cultural sector. The findings argue for a coordinated effort to embed emerging technologies (especially AI) within museum training pathways, ensuring future professionals are equipped to design inclusive, engaging, and technologically informed museum experiences. This comparative perspective offers actionable insights for educators, policymakers, and cultural institutions seeking to advance digital capacity-building in museum practice.

LLMs as ID Co-Pilots: Automating Routine Tasks to Elevate Strategic Instructional Design and Pedagogy

Yijun (Diana) Dai, Columbia University, New York, New York, USA

Instructional Designers (IDs) often dedicate significant time to repetitive course development tasks, diverting focus from strategic pedagogical innovation. This session presents a real-world project that piloted Large Language Model (LLM) integration to transform the ID workflow, positioning LLMs as co-pilots.

The initiative emphasized collective learning and systematic integration across the ID team. Key deliverables, designed to improve efficiency and consistency, included a high-level workflow map to identify precise AI integration points and a categorized prompt library for common ID tasks, such as formatting syllabus and drafting assessment rubrics. We also developed and tested a Custom AI Assistant (GPT/Gemini-based) tailored to specific team processes. A critical component of the project was the creation and deployment of ethical and responsible use guidelines to govern AI application in design and faculty collaboration…

Keywords: Instructional Design (ID), Large Language Models (LLMs), AI in Education, Workflow Optimization, Prompt Engineering

LLMs as ID Co-Pilots: Automating Routine Tasks to Elevate Strategic Instructional Design and Pedagogy

Yijun (Diana) Dai

Instructional Designers (IDs) often dedicate significant time to repetitive course development tasks, diverting focus from strategic pedagogical innovation. This session presents a real-world project that piloted Large Language Model (LLM) integration to transform the ID workflow, positioning LLMs as co-pilots.

The initiative emphasized collective learning and systematic integration across the ID team. Key deliverables, designed to improve efficiency and consistency, included a high-level workflow map to identify precise AI integration points and a categorized prompt library for common ID tasks, such as formatting syllabus and drafting assessment rubrics. We also developed and tested a Custom AI Assistant (GPT/Gemini-based) tailored to specific team processes. A critical component of the project was the creation and deployment of ethical and responsible use guidelines to govern AI application in design and faculty collaboration.

Attendees will learn how to replicate this structured approach, gaining insight into the tools and processes required to successfully implement AI-driven efficiencies. I will share the project's success metrics and practical strategies for shifting IDs from tactical content executors to strategic pedagogical leaders, ultimately driving innovation in course design.

Digital Multimodal Composing in the Age of AI

Zeynep Daşer, Ataturk University, Erzurum, Turkey

The swift advancement of digital technologies has significantly reshaped the second language (L2) writing process, particularly with the introduction of digital multimodal composition (DMC), which offers a creative literacy practice where meaning is redesigned by bringing together multiple semiotic modes. While DMC has been commonly explored through the lenses of multimodality and multiliteracies, as well as socio-cultural frameworks, the emergence of artificial intelligence (AI) tools and their integration with DMC projects opens new avenues for more engaging and adjustable learning opportunities. This conceptual paper seeks to examine current theoretical and pedagogical perspectives on how cutting-edge AI technologies are transforming DMC practices in L2 writing classes.

Guided by the previously stated theories and recent scholarly works on AI-facilitated DMC in L2 writing, this paper contends that DMC in the age of AI represents a writing process that extends beyond a mere shift from traditional monomodal writing; instead, it embodies a new conceptual understanding of writing in a foreign language…

Keywords: Digital Multimodal Composing, Artificial Intelligence, Multimodality, Multiliteracies, Socio-Cultural Theory

Digital Multimodal Composing in the Age of AI

Zeynep Daşer

The swift advancement of digital technologies has significantly reshaped the second language (L2) writing process, particularly with the introduction of digital multimodal composition (DMC), which offers a creative literacy practice where meaning is redesigned by bringing together multiple semiotic modes. While DMC has been commonly explored through the lenses of multimodality and multiliteracies, as well as socio-cultural frameworks, the emergence of artificial intelligence (AI) tools and their integration with DMC projects opens new avenues for more engaging and adjustable learning opportunities. This conceptual paper seeks to examine current theoretical and pedagogical perspectives on how cutting-edge AI technologies are transforming DMC practices in L2 writing classes.

Guided by the previously stated theories and recent scholarly works on AI-facilitated DMC in L2 writing, this paper contends that DMC in the age of AI represents a writing process that extends beyond a mere shift from traditional monomodal writing; instead, it embodies a new conceptual understanding of writing in a foreign language. Functioning both as digital partners and collaborators, AI technologies help learners deal with the setbacks they encounter during the challenging DMC projects by generating the necessary sources to decrease their cognitive load. They also create a novel form of learning environment in which students socially interact with each other and learn from their peers, instructors, and AI-generated insights, thereby fostering a more motivating writing process. By conceptualizing DMC within AI-powered literacy practices, this paper acknowledges the potential affordances of AI tools in promoting more creative DMC tasks, provides useful pedagogical insights vis-à-vis the informed uses of AI in multimodal L2 writing instruction to harness the potential of AI tools, and highlights the need for carefully designed DMC tasks that are assisted by AI technologies.

IGIP SESSION

Engineering Education in the Digital Age: A Bridge Between Technological Innovation and Advanced Training

Carlo De Medio, Tor Vergata, Rome, Italy

This session discusses the important relationship between technology and teaching methods and provides an approach to using technology to enhance the learning experience and develop necessary skills. The authors describe how artificial intelligence, virtual reality and other immersive technologies, tactile systems, the Internet of Things (IoT), and three-dimensional modeling have changed the way we personalize student learning, engage students in the classroom, and increase access to education. Of particular interest to the authors is how these technologies can be used to provide personalized learning in institutions such as museums; Integrating these new technologies into existing teaching methods, such as object-based learning, will enable students to gain a deeper understanding of the subject matter and develop the analytical, problem-solving, and collaborative skills necessary to succeed in the 21st century…

Keywords: Engineering Pedagogy, Immersive Technologies, Artificial Intelligence, Museum Learning, Skills Development

Engineering Education in the Digital Age: A Bridge Between Technological Innovation and Advanced Training

Carlo De Medio

This session discusses the important relationship between technology and teaching methods and provides an approach to using technology to enhance the learning experience and develop necessary skills. The authors describe how artificial intelligence, virtual reality and other immersive technologies, tactile systems, the Internet of Things (IoT), and three-dimensional modeling have changed the way we personalize student learning, engage students in the classroom, and increase access to education. Of particular interest to the authors is how these technologies can be used to provide personalized learning in institutions such as museums; Integrating these new technologies into existing teaching methods, such as object-based learning, will enable students to gain a deeper understanding of the subject matter and develop the analytical, problem-solving, and collaborative skills necessary to succeed in the 21st century.

However, implementing these technologies effectively requires a solid pedagogical foundation and a strategic plan. The plan should include funding models, organizational change management, and ongoing professional development for faculty members. It is therefore essential to use experience in developing targeted pedagogy to ensure that digital innovation is strategically aligned with educational goals in order to prepare the next generation of students and innovators in a rapidly changing technological environment.

Building Community Connections through Immersive Service-Learning for Upper-Level Spanish Students

Sara De Nicolas, Ph.D., High Point University, High Point, North Carolina, USA

This presentation highlights innovative strategies I have developed as a college professor to engage upper-level Spanish majors and minors through community-based, service-learning experiences that enhance language proficiency and intercultural competence. Over several semesters, I have organized and taught these courses, creating lesson materials and projects that provide students with authentic, hands-on learning opportunities. Partnerships with Spanish immersion local schools allow students to interact with native speakers, collaborate with educators, and design lessons for young learners. These experiences foster meaningful engagement and have consistently received positive feedback from students, who value the real-world application of their language skills and the opportunity to build connections across generations.

The courses also encourage students to create original content, including lesson plans, culturally responsive materials, and digital resources that can be shared across platforms, strengthening their ties to the broader community…

Keywords: Spanish Language Education, Service Learning, Community Engagement, Experiential Learning, Immersion Programs

Building Community Connections through Immersive Service-Learning for Upper-Level Spanish Students

Sara De Nicolas, Ph.D.

This presentation highlights innovative strategies I have developed as a college professor to engage upper-level Spanish majors and minors through community-based, service-learning experiences that enhance language proficiency and intercultural competence. Over several semesters, I have organized and taught these courses, creating lesson materials and projects that provide students with authentic, hands-on learning opportunities. Partnerships with Spanish immersion local schools allow students to interact with native speakers, collaborate with educators, and design lessons for young learners. These experiences foster meaningful engagement and have consistently received positive feedback from students, who value the real-world application of their language skills and the opportunity to build connections across generations.

The courses also encourage students to create original content, including lesson plans, culturally responsive materials, and digital resources that can be shared across platforms, strengthening their ties to the broader community. This experiential approach promotes deeper retention of language structures, pragmatics, and sociolinguistic awareness while cultivating professional skills in education, translation, and community outreach. Students develop empathy, leadership, and adaptability—competencies highly valued in academic and professional settings.

In my presentation, I will share insights from designing, implementing, and assessing these service-learning programs, including strategies for fostering meaningful interactions with native speakers and young learners. I will also discuss how these immersive experiences bridge university-level education with local communities and create sustainable partnerships that benefit both students and schools.

IGIP SESSION

Enhancing Graduate Employability by Developing Professional Skills and Gaining Practical Experience through an Educational Program in Electronic Test Engineering

Serge Demidenko, Ph.D., and Moi Tin Chew, Ph.D., Sunway University, Selangor, Malaysia and Melanie Po-Leen Ooi, Ph.D., and Ye Chow Kuang, Ph.D., University of Waikato, Hamilton, New Zealand

The global semiconductor industry faces a growing shortage of skilled technical personnel. This issue is driven by the increasing complexity of electronic circuits and a systemic mismatch between engineering education and industry needs. Consequently, the skills gap between recent graduates’ technical abilities and employer requirements has become a significant bottleneck.

Electronic testing, which is crucial throughout semiconductor device manufacturing, is one of the areas of particular concern. The education and training of future test specialists have been a continuous focus of research and development since the advent of integrated semiconductor technology.

This presentation proposal is based on years of developing practical solutions through close collaboration with major industry players. Such collaboration has been highly valuable, providing access to…

Keywords: Semiconductor Industry, Electronic Testing, Engineering Education

Enhancing Graduate Employability by Developing Professional Skills and Gaining Practical Experience through an Educational Program in Electronic Test Engineering

Serge Demidenko, Ph.D., Moi Tin Chew, Ph.D., Melanie Po-Leen Ooi, Ph.D., and Ye Chow Kuang, Ph.D.

The global semiconductor industry faces a growing shortage of skilled technical personnel. This issue is driven by the increasing complexity of electronic circuits and a systemic mismatch between engineering education and industry needs. Consequently, the skills gap between recent graduates’ technical abilities and employer requirements has become a significant bottleneck.

Electronic testing, which is crucial throughout semiconductor device manufacturing, is one of the areas of particular concern. The education and training of future test specialists have been a continuous focus of research and development since the advent of integrated semiconductor technology.

This presentation proposal is based on years of developing practical solutions through close collaboration with major industry players. Such collaboration has been highly valuable, providing access to real-world equipment, expertise, and practical experience, and fostering an understanding of industry expectations for the capabilities of test technology staff. It has led to the development, implementation, validation, and ongoing improvement of a comprehensive educational framework in electronic test engineering. This framework includes a test technology course delivered jointly with industry engineering staff, laboratory and mini-project series, industry internships, and a year-long capstone project co-designed and co-supervised with industry. It has been integrated into undergraduate and postgraduate programs at several international universities and has successfully trained cohorts of job-ready test engineers, directly addressing the industry's workforce crisis.

A family of low-cost, portable test systems supports the laboratory training and development of mini-projects within this framework. It offers a scalable, practical solution for test technology education while removing the high costs associated with purchasing, maintaining, operating, and integrating industrial Automated Test Equipment (ATE) into university curricula. The systems combine universal configurable hardware and software platforms, specialized electronic modules, and virtual instrumentation tools. They are paired with a project-based curriculum. Students are tasked with developing operational testers that emulate industrial ATE and applying them to test real-world semiconductor devices. The systems can operate independently as standalone educational and training tools, making them suitable also for targeted short professional retraining and micro-credential courses.

Reversing the Paradigm: How Generative AI Is Reshaping Software Engineering Learning, Competencies, Performance

Gary Dickelman, EPSScentral LLC, Boynton Beach, Florida, USA

Generative AI tools like GitHub Copilot, ChatGPT, and Claude.ai have disrupted software development, challenging how we educate, train, and assess software engineering competencies. Unlike previous technological shifts that amplified advantages for highly skilled workers, research reveals that AI-assisted development disproportionately benefits novice developers, with productivity gains reaching 34-55% for entry-level workers compared to minimal improvements for experts.

This study synthesizes recent research in three interconnected transformations: (1) Educational curriculum redesign, where leading universities now introduce AI coding assistants before students write their first line of code, fundamentally altering what learning to program means; (2) Organizational responses, as businesses shift from credential-based to skills-based hiring while creating entirely new role categories (Prompt Engineers, LLM Operations Engineers (LLMOps), Synthetic Data Engineers, Quality and Governance Engineers, etc.), and navigating the 97% adoption rate among IT professionals; and (3) Economic restructuring, with projections suggesting $2.6-4.4 trillion in annual created value but significant uncertainty about employment trajectories…

Keywords: Learning, Performance, Software Engineering, Competencies, Education

Reversing the Paradigm: How Generative AI Is Reshaping Software Engineering Learning, Competencies, Performance

Gary Dickelman

Generative AI tools like GitHub Copilot, ChatGPT, and Claude.ai have disrupted software development, challenging how we educate, train, and assess software engineering competencies. Unlike previous technological shifts that amplified advantages for highly skilled workers, research reveals that AI-assisted development disproportionately benefits novice developers, with productivity gains reaching 34-55% for entry-level workers compared to minimal improvements for experts.

This study synthesizes recent research in three interconnected transformations: (1) Educational curriculum redesign, where leading universities now introduce AI coding assistants before students write their first line of code, fundamentally altering what learning to program means; (2) Organizational responses, as businesses shift from credential-based to skills-based hiring while creating entirely new role categories (Prompt Engineers, LLM Operations Engineers (LLMOps), Synthetic Data Engineers, Quality and Governance Engineers, etc.), and navigating the 97% adoption rate among IT professionals; and (3) Economic restructuring, with projections suggesting $2.6-4.4 trillion in annual created value but significant uncertainty about employment trajectories.

Drawing on controlled experiments, large-scale field studies, and curriculum analyses from leading institutions (e.g., MIT, Stanford, Carnegie Mellon), this study explores critical implications for learning and performance professionals. Key findings suggest that AI tools capture and disseminating tacit knowledge from top performers, effectively democratizing expertise that once required years to develop. However, this raises questions: How do we assess authentic learning when AI completes assignments and projects? What foundational skills remain essential? How should compensation reflect workers' contributions to AI training data? How do educational institutions and the workplace distinguish and place appropriate value on organic versus synthetic advances in software engineering?

The study concludes with practical frameworks for educators, corporate trainers, and learning & performance professionals navigating this transition, including evidence-based strategies for integrating AI tools while preserving critical thinking, ethical reasoning, and deep learning & performance outcomes.

Redefining Aging: Empowering Seniors through Innovation, Inclusion, and Community—A Comprehensive Approach to Enhancing the Lives of Seniors

Nadra Dunbar, Silver Venere Group, Garfield Heights, Ohio, USA

This session aims to redefine aging as a phase marked by vitality, contribution, and dignity. The presenter advocates for creating inclusive communities that support seniors' health, purpose, and well-being through innovative, research-based strategies inspired by Blue Zones principles and other research.

The presenter's goal is on shifting the narrative from decline to empowerment and emphasizes the importance of equity, community engagement, sustainability, and ethical innovation. The presentation will highlight efforts to develop best practices both domestically and globally, enabling seniors to lead healthier, longer lives…

Keywords: Informative, Participatory, Purposeful

Redefining Aging: Empowering Seniors through Innovation, Inclusion, and Community—A Comprehensive Approach to Enhancing the Lives of Seniors

Nadra Dunbar

This session aims to redefine aging as a phase marked by vitality, contribution, and dignity. The presenter advocates for creating inclusive communities that support seniors' health, purpose, and well-being through innovative, research-based strategies inspired by Blue Zones principles and other research.

The presenter's goal is on shifting the narrative from decline to empowerment and emphasizes the importance of equity, community engagement, sustainability, and ethical innovation. The presentation will highlight efforts to develop best practices both domestically and globally, enabling seniors to lead healthier, longer lives.

A key aspect of her approach is promoting senior self-agency—empowering older adults to actively influence their environments and lifestyles for improved wellness outcomes. The session will include interactive components to encourage audience participation, fostering understanding and collaboration.

Furthermore, the presenter underscores the necessity of tailored strategies for urban and rural environments by highlighting accessible community centers, technology-driven social engagement, and readily available healthcare services along with efforts on improving connectivity, transportation, internet access, and leveraging local community strengths to maintain purpose and agency among seniors.

Universal Design for Learning (UDL) Lecture Design with AI: An Interactive Workshop

Srinivasan Durairaj, Ph.D., Richland Community College, Decatur, Illinois, USA

Today's classrooms demand innovative strategies to address the challenges of diverse student populations, rapid digital transformation, and student well being. This 60-minute interactive session invites educators to redesign an existing lecture or topic using AI-enhanced Universal Design for Learning (UDL) strategies. This pedagogical approach is based on my teaching experience in India, Fiji, and the USA, which has demonstrated the critical need for flexible and accessible instruction.

We begin by identifying common challenges with Gen Z learners, such as short attention spans, digital overload, and varying levels of prior knowledge. Building on this, the facilitator will model a clear step-by-step UDL workflow centered on the three principles: engagement, representation, and action/expression. Using their own course materials, attendees will experiment with user-friendly AI tools (e.g., ChatGPT, NotebookLM) to create multimodal resources such as visual organizers, audio explanations, and accessible handouts…

Keywords: Universal Design for Learning (UDL), Artificial Intelligence in Education, Inclusive Teaching Strategies, Gen Z Learners

Universal Design for Learning (UDL) Lecture Design with AI: An Interactive Workshop

Srinivasan Durairaj, Ph.D.

Today's classrooms demand innovative strategies to address the challenges of diverse student populations, rapid digital transformation, and student well being. This 60-minute interactive session invites educators to redesign an existing lecture or topic using AI-enhanced Universal Design for Learning (UDL) strategies. This pedagogical approach is based on my teaching experience in India, Fiji, and the USA, which has demonstrated the critical need for flexible and accessible instruction.

We begin by identifying common challenges with Gen Z learners, such as short attention spans, digital overload, and varying levels of prior knowledge. Building on this, the facilitator will model a clear step-by-step UDL workflow centered on the three principles: engagement, representation, and action/expression. Using their own course materials, attendees will experiment with user-friendly AI tools (e.g., ChatGPT, NotebookLM) to create multimodal resources such as visual organizers, audio explanations, and accessible handouts.

Most of the session is hands-on small group work. Participants will:

Apply the modeled workflow to their own lectures or topics.

Use AI to co-create multimodal resources tailored to diverse learner needs.

Exchange and critique drafts with peers, focusing on clarity, inclusivity, and feasibility.

Throughout the demonstration, the session will embed simple practices for transparent, ethical AI use and integrating these tools into ongoing formative assessment and learner support. Attendees will leave with (1) at least one redesigned, UDL-informed lecture segment ready to pilot; (2) a reusable, step-by-step AI workflow adaptable to any topic; and (3) a concise set of strategies for sustaining engagement, enhancing accessibility, and promoting student agency in their own courses.

Students using NotebookLM to Conduct Research, Learn, and Present Why Businesses Fail

David Ecker, Stony Brook University, Stony Brook, New York, USA

Students today are great at finding information, but they struggle to make sense of it, connect ideas, and explain what they’ve learned clearly. This demonstration shows how NotebookLM can change that. I’ll walk through an activity I use with my entrepreneurship students, where they investigate why businesses fail by gathering sources, uploading them into NotebookLM, generating a short learning podcast, and then turning those insights into a 60-second elevator pitch.

In this session, you’ll see precisely how the process works. Understanding how they can take messy research and turn it into clear understanding is helping students think more critically. I’ll demonstrate how NotebookLM supports deeper comprehension, helps students notice failure patterns, and pushes them to generate stronger ideas. This also shows how we can use AI tools in our classrooms without adding a ton of extra work.

By attending, I want you to learn how NotebookLM works and see how you might use it in your own classes.

Keywords: Notebook LM, Student Learning, AI in Education, Entrepreneurship

Students using NotebookLM to Conduct Research, Learn, and Present Why Businesses Fail

David Ecker

Students today are great at finding information, but they struggle to make sense of it, connect ideas, and explain what they’ve learned clearly. This demonstration shows how NotebookLM can change that. I’ll walk through an activity I use with my entrepreneurship students, where they investigate why businesses fail by gathering sources, uploading them into NotebookLM, generating a short learning podcast, and then turning those insights into a 60-second elevator pitch.

In this session, you’ll see precisely how the process works. Understanding how they can take messy research and turn it into clear understanding is helping students think more critically. I’ll demonstrate how NotebookLM supports deeper comprehension, helps students notice failure patterns, and pushes them to generate stronger ideas. This also shows how we can use AI tools in our classrooms without adding a ton of extra work.

By attending, I want you to learn how NotebookLM works and see how you might use it in your own classes.

A Structured Instructional Model for Game Development: Integrating Lecture, Video, and Lab to Scaffold Complex Learning

Mustafa Elfituri, Ph.D., Khalfalla Awedat, Ph.D., and James Verity, SUNY Morrisville, Fayetteville, New York, USA

Teaching game development requires students to master complex tools, design processes, and multi-step workflows, presenting a significant pedagogical challenge in traditional classroom settings. To address this challenge, we designed and evaluated a structured instructional model designed to scaffold learning. The model integrates three core components: short introductory lectures to establish context, guided video tutorials for self-paced procedural learning, and hands-on lab activities for immediate application. This sequence culminated in a final project requiring the synthesis of skills to design and implement an original game.

To assess the model’s effectiveness, a comprehensive survey was administered to students (n=49) upon course completion. Preliminary results indicate a substantial positive impact. Most notably, students’ self-reported confidence in creating games showed a significant increase, with average ratings rising from 2.60 (pre-course) to 3.70 (post-course)…

Keywords: Game-Based Learning, Instructional Design, Blended Learning, Technical Education, Self-Paced Learning

A Structured Instructional Model for Game Development: Integrating Lecture, Video, and Lab to Scaffold Complex Learning

Mustafa Elfituri, Ph.D., Khalfalla Awedat, Ph.D., and James Verity

Teaching game development requires students to master complex tools, design processes, and multi-step workflows, presenting a significant pedagogical challenge in traditional classroom settings. To address this challenge, we designed and evaluated a structured instructional model designed to scaffold learning. The model integrates three core components: short introductory lectures to establish context, guided video tutorials for self-paced procedural learning, and hands-on lab activities for immediate application. This sequence culminated in a final project requiring the synthesis of skills to design and implement an original game.

To assess the model’s effectiveness, a comprehensive survey was administered to students (n=49) upon course completion. Preliminary results indicate a substantial positive impact. Most notably, students’ self-reported confidence in creating games showed a significant increase, with average ratings rising from 2.60 (pre-course) to 3.70 (post-course). Participants also rated the video tutorials highly for clarity and utility, with average scores of 3.73 and 3.98 out of 5, respectively. Qualitative feedback emphasized the value of the self-paced video component and confirmed that the structured breakdown of tasks enhanced clarity and independent problem-solving.

This presentation will detail the instructional framework, present the analysis of survey findings, and discuss the implications for designing effective pedagogy in technically demanding and creative disciplines. The model offers a replicable strategy for enhancing skill acquisition, confidence, and creative synthesis in project-based learning environments.

IGIP SESSION

The Integration of AI-Driven Feedback Systems in Mathematics Teaching

Russina Eltoum, Ph.D., Prince Mohammad Bin Fahd University, Al-Khobar, Saudi Arabia

This study investigates the integration of AI-driven feedback systems in mathematics teaching through the use of Pearson My Lab Math in Precalculus course at Prince Mohammed Bin Fahd University (PMU). Utilizing a Precalculus course coordination data of four semesters, conceptual understanding, and assessment preference across MyLab-based and traditional paper-based modes. Quantitative analyses of Course Learning Outcome (CLO) results reveal significant improvements in procedural fluency and steady gains in overall achievement among students who actively engaged with the adaptive and automated feedback features of the platform. However, the improvement in the conceptual understanding was modest, which indicates the need for the instructors’ feedback. Students feedback highlighted the advantages of the instant and personalized feedback while emphasizing the continued need for human intervention and guidance. The study contributes to discussions of AI in higher education when integrated within a blended pedagogical model.

Keywords: AI-Driven Feedback, Mathematics Education, Digital Assessment, Learning Analytics, Higher Education

The Integration of AI-Driven Feedback Systems in Mathematics Teaching

Russina Eltoum, Ph.D.

This study investigates the integration of AI-driven feedback systems in mathematics teaching through the use of Pearson My Lab Math in Precalculus course at Prince Mohammed Bin Fahd University (PMU). Utilizing a Precalculus course coordination data of four semesters, conceptual understanding, and assessment preference across MyLab-based and traditional paper-based modes. Quantitative analyses of Course Learning Outcome (CLO) results reveal significant improvements in procedural fluency and steady gains in overall achievement among students who actively engaged with the adaptive and automated feedback features of the platform. However, the improvement in the conceptual understanding was modest, which indicates the need for the instructors’ feedback. Students feedback highlighted the advantages of the instant and personalized feedback while emphasizing the continued need for human intervention and guidance. The study contributes to discussions of AI in higher education when integrated within a blended pedagogical model.

Preparing Computer Science Students for the AI-Driven Workplace: Bridging Academia and Industry through Curriculum Innovation

Maryam Etezad, Ph.D., Chapman University, Newport Coast, California, USA

The rapid adoption of artificial intelligence (AI) across the engineering and computing industries is fundamentally reshaping the skills required of computer science graduates. This transformation has also introduced growing uncertainty among students, particularly those pursuing computer science, who are increasingly concerned about the impact of AI on future job prospects. At the same time, many higher education programs have not yet fully adapted their curricula to reflect these shifts, contributing to a widening gap between academic preparation and workforce expectations.

This study investigates how computer science education can evolve to better prepare students for AI-augmented professional environments while addressing student concerns about career readiness. We conducted semi-structured interviews with faculty in computer science and engineering, as well as industry professionals actively integrating AI tools into software development and data-driven workflows. Using thematic analysis, we identify key emerging competencies, including…

Keywords: Artificial Intelligence in Higher Education, Curriculum Innovation, Workforce Readiness, Experiential and Project-Based Learning, Human–AI Collaboration in Learning

Preparing Computer Science Students for the AI-Driven Workplace: Bridging Academia and Industry through Curriculum Innovation

Maryam Etezad, Ph.D.

The rapid adoption of artificial intelligence (AI) across the engineering and computing industries is fundamentally reshaping the skills required of computer science graduates. This transformation has also introduced growing uncertainty among students, particularly those pursuing computer science, who are increasingly concerned about the impact of AI on future job prospects. At the same time, many higher education programs have not yet fully adapted their curricula to reflect these shifts, contributing to a widening gap between academic preparation and workforce expectations.

This study investigates how computer science education can evolve to better prepare students for AI-augmented professional environments while addressing student concerns about career readiness. We conducted semi-structured interviews with faculty in computer science and engineering, as well as industry professionals actively integrating AI tools into software development and data-driven workflows. Using thematic analysis, we identify key emerging competencies, including AI-assisted coding, prompt engineering, critical evaluation of AI-generated outputs, and ethical considerations in AI use.

Based on these findings, we propose a practical framework for integrating AI into undergraduate computer science curricula to better align with current industry needs. Our recommendations include course redesign strategies, the incorporation of AI tools into project-based learning, and approaches for fostering effective human–AI collaboration skills. We also present initial implementation examples from our program, demonstrating how targeted curricular innovations can bridge the gap between academic learning and real-world practice.

This work provides actionable guidance for educators seeking to modernize computing curricula and supports the development of graduates who are adaptable, confident, and prepared to thrive in AI-driven workplaces.

Exploring Generative AI Use in the Workplace and Higher Education for UX/LX Design

Helen Fake, Ph.D., Flexion, Inc. and George Mason University, Falls Church, Virginia, USA; Lisa Giacumo, Ph.D., George Mason University, Fairfax, Virginia, USA; and Shontá Bradford, Flexion, Inc., Austin, Texas, USA

Many studies suggest that Generative AI use is becoming integral to the UX/LXD work process (Li, Cao, Lin, Hou, Zhu, Ali, 2024; Lu, Yang, Zhao, Zhang, Jia-Jun Li, 2024). There is less clarity, however, about how this innovative technology is being used in the UX/LX Design Process.

To better understand Generative AI use for UX/LXD students and professionals in their design process, a survey was administered in Fall of 2025 (N=48). Preliminary findings suggest that students and professionals are using GenAI the most for (1) exploratory research planning, (2) data analysis and synthesis, (3) creating user stories, and (4) presenting results. Responses also indicated that participants felt that LLMs were particularly important to completing the following tasks: (1) drafting, content creation, or design and development, (2) brainstorming, (3) editing, and (4) learning. A discussion of the results explores existing opportunities and gaps between the two groups which will ultimately be used to help inform the learning activities and curriculum design of future UX/LXD courses.

Keywords: Generative AI, User Experience Design, Learning Experience Design, Iteration, Instructional Course Design

Exploring Generative AI Use in the Workplace and Higher Education for UX/LX Design

Helen Fake, Ph.D., Lisa Giacumo, Ph.D., and Shontá Bradford

Many studies suggest that Generative AI use is becoming integral to the UX/LXD work process (Li, Cao, Lin, Hou, Zhu, Ali, 2024; Lu, Yang, Zhao, Zhang, Jia-Jun Li, 2024). There is less clarity, however, about how this innovative technology is being used in the UX/LX Design Process.

To better understand Generative AI use for UX/LXD students and professionals in their design process, a survey was administered in Fall of 2025 (N=48). Preliminary findings suggest that students and professionals are using GenAI the most for (1) exploratory research planning, (2) data analysis and synthesis, (3) creating user stories, and (4) presenting results. Responses also indicated that participants felt that LLMs were particularly important to completing the following tasks: (1) drafting, content creation, or design and development, (2) brainstorming, (3) editing, and (4) learning. A discussion of the results explores existing opportunities and gaps between the two groups which will ultimately be used to help inform the learning activities and curriculum design of future UX/LXD courses.

Leveraging AI and the Personalized Learning Interaction Framework to Personalize Training Programs at Scale

Helen Fake, Ph.D.,Flexion, Inc. and George Mason University, Falls Church, Virginia, USA and Nada Dabbagh, Ph.D.,George Mason University, Fairfax, Virginia, USA

The ongoing excitement surrounding emerging technologies has reignited interest in scaling personalized learning in meaningful ways (Bersin, 2021; Luo, Qin, Fang, & Qu, 2021). Despite this enthusiasm, however, there is little formal direction on how to implement personalized learning at scale for Workforce Training and Development programs, and specifically in a way that is also both sustainable and learner-centered. Drawing on an extensive and ongoing body of research to include a foundational Delphi study with 224 expert participants, a CLO survey, peer-reviewed publications, informal organizational and formalized international workshops, and a recently released book, The Personalized Learning Interaction Framework (PLIF) seeks to provide this guidance for CLOs and other learning leaders.

Grounded in a rigorous expert review process, the PLIF positions personalized learning as an exercise requiring a multitude of content and social, or socio-technical driven learning interactions. The framework identifies six different connections to support comprehensive personalized learning environments…

Keywords: Personalized Learning, Workforce Training and Development, Training, Training and Development, Adult Learning

Leveraging AI and the Personalized Learning Interaction Framework to Personalize Training Programs at Scale

Helen Fake, Ph.D., and Nada Dabbagh, Ph.D.

The ongoing excitement surrounding emerging technologies has reignited interest in scaling personalized learning in meaningful ways (Bersin, 2021; Luo, Qin, Fang, & Qu, 2021). Despite this enthusiasm, however, there is little formal direction on how to implement personalized learning at scale for Workforce Training and Development programs, and specifically in a way that is also both sustainable and learner-centered. Drawing on an extensive and ongoing body of research to include a foundational Delphi study with 224 expert participants, a CLO survey, peer-reviewed publications, informal organizational and formalized international workshops, and a recently released book, The Personalized Learning Interaction Framework (PLIF) seeks to provide this guidance for CLOs and other learning leaders.

Grounded in a rigorous expert review process, the PLIF positions personalized learning as an exercise requiring a multitude of content and social, or socio-technical driven learning interactions. The framework identifies six different connections to support comprehensive personalized learning environments to include ones between learners and content, among peers, within small groups, with mentors or AI-based coaches, through connected devices, and across broader communities of practice and social networks.

This demonstration introduces participants to the PLIF framework and encourages an analysis of their organizational programs' existing offerings to define the opportunities and gaps that currently exist as well as an action plan to increase access to personalized learning interactions. Within the demonstration, the ways that AI might further strengthen, facilitate, and optimize these interactions will be presented. By the end, participants will have concrete strategies to align AI capabilities and human-centered design principles in building more adaptive and effective learning ecosystems.

Enhancing Faculty Feedback Quality and Consistency with Generative AI

Negar Farakish, Ed.D., and Hui Soo Chae, Ed.D., New York University, New York, New York, USA

High-quality feedback is one of the strongest predictors of student learning, yet it is also among the most difficult instructional practices to sustain consistently across students, assignments, and time. This session examines how Generative AI (GenAI) can be used to enhance the quality, clarity, and consistency of faculty feedback without automating judgment or minimizing the faculty voice. This demonstration-based presentation shows how GenAI tools can be integrated into existing feedback workflows to support formative, revision-oriented learning. Through concrete examples, the presenters will illustrate how GenAI can help faculty align feedback more closely with rubrics and learning outcomes, reduce repetitive commentary, and identify patterns in student work that enable more targeted and equitable responses…

Keywords: Generative AI, Human-Centered AI, AI-Supported Feedback

Enhancing Faculty Feedback Quality and Consistency with Generative AI

Negar Farakish, Ed.D., and Hui Soo Chae, Ed.D.

High-quality feedback is one of the strongest predictors of student learning, yet it is also among the most difficult instructional practices to sustain consistently across students, assignments, and time. This session examines how Generative AI (GenAI) can be used to enhance the quality, clarity, and consistency of faculty feedback without automating judgment or minimizing the faculty voice. This demonstration-based presentation shows how GenAI tools can be integrated into existing feedback workflows to support formative, revision-oriented learning. Through concrete examples, the presenters will illustrate how GenAI can help faculty align feedback more closely with rubrics and learning outcomes, reduce repetitive commentary, and identify patterns in student work that enable more targeted and equitable responses. In this model, GenAI functions as a feedback amplifier, not a grading engine. By the end of the session, participants will leave with a clear conceptual framework for using GenAI to enhance feedback quality and consistency, an understanding of the tools and design decisions involved, and practical guidance for piloting and evaluating AI-supported feedback approaches in their own courses.

Building Support for Students through Generative AI Course Assistants

Negar Farakish, Ed.D., and Hui Soo Chae, Ed.D., New York University, New York, New York, USA

As Generative AI (GenAI) becomes a permanent feature of teaching and learning environments, faculty have a unique opportunity to shape how these tools support student learning in intentional and pedagogically meaningful ways. This session is a demonstration-based presentation that shows how GenAI course assistants can be created using readily available AI tools and platforms such as Google Gemini and NotebookLM. Through concrete examples, the presenters will illustrate how AI-course assistants can be grounded in materials such as course syllabi, assignments, rubrics, and learning resources to provide consistent, just-in-time support for students. Attendees will see examples of effective prompt design, content grounding, and guardrails that ensure the assistant functions as a learning companion rather than an answer engine. By the end of the session, participants will leave with a clear understanding of the tools and design decisions involved, along with practical guidance for creating similar GenAI course assistants within their own courses.

Keywords: Generative AI Course Assistants, Teaching with Generative AI, AI-Enabled Student Support

Building Support for Students through Generative AI Course Assistants

Negar Farakish, Ed.D., and Hui Soo Chae, Ed.D.

As Generative AI (GenAI) becomes a permanent feature of teaching and learning environments, faculty have a unique opportunity to shape how these tools support student learning in intentional and pedagogically meaningful ways. This session is a demonstration-based presentation that shows how GenAI course assistants can be created using readily available AI tools and platforms such as Google Gemini and NotebookLM. Through concrete examples, the presenters will illustrate how AI-course assistants can be grounded in materials such as course syllabi, assignments, rubrics, and learning resources to provide consistent, just-in-time support for students. Attendees will see examples of effective prompt design, content grounding, and guardrails that ensure the assistant functions as a learning companion rather than an answer engine. By the end of the session, participants will leave with a clear understanding of the tools and design decisions involved, along with practical guidance for creating similar GenAI course assistants within their own courses.

Sustainable Learning Ecosystems for Human-Centered AI in Intergenerational Digital Workplaces

Yuliia Fedorova, Ph.D., Oleksii Ilchenko and Juraj Mikus, Comenius University Bratislava, Bratislava Region, Slovakia and Denys Kovalenko, Educational and Scientific Institute "Ukrainian Engineering and Pedagogical Academy", Kharkiv, Ukraine

Rapid advances in artificial intelligence (AI) are transforming how work is performed, monitored, and optimized, yet learning practices in both higher education and workplaces often remain fragmented, tool-centric, and insufficiently aligned with human needs. This gap is particularly visible in intergenerational digital workplaces, where employees differ significantly in experience, learning preferences, emotional expectations, and trust in AI-driven systems. This paper proposes a conceptual framework for Sustainable Learning Ecosystems for Human-Centered AI, aimed at reimagining how learning, technology, and human agency can be integrated across education and work contexts.

The proposed ecosystem shifts the focus from isolated training interventions toward continuous, adaptive learning embedded directly within digital work processes. By combining human-centered AI, real-time process feedback, and AI-augmented learning and simulation mechanisms, the framework supports learning through reflection, prediction, and decision-making rather than passive content consumption…

Keywords: Human-Centered AI, Sustainable Learning Ecosystems, Emotional Intelligence, AI-Augmented Learning, Human–AI Collaboration

Sustainable Learning Ecosystems for Human-Centered AI in Intergenerational Digital Workplaces

Yuliia Fedorova, Ph.D., Oleksii Ilchenko, Denys Kovalenko and Juraj Mikus

Rapid advances in artificial intelligence (AI) are transforming how work is performed, monitored, and optimized, yet learning practices in both higher education and workplaces often remain fragmented, tool-centric, and insufficiently aligned with human needs. This gap is particularly visible in intergenerational digital workplaces, where employees differ significantly in experience, learning preferences, emotional expectations, and trust in AI-driven systems. This paper proposes a conceptual framework for Sustainable Learning Ecosystems for Human-Centered AI, aimed at reimagining how learning, technology, and human agency can be integrated across education and work contexts.

The proposed ecosystem shifts the focus from isolated training interventions toward continuous, adaptive learning embedded directly within digital work processes. By combining human-centered AI, real-time process feedback, and AI-augmented learning and simulation mechanisms, the framework supports learning through reflection, prediction, and decision-making rather than passive content consumption. Emotional intelligence and intergenerational collaboration are treated as core design principles, ensuring that AI feedback is interpretable, supportive, and sensitive to diverse cognitive and emotional responses.

The framework further bridges higher education and workplace learning by positioning universities as preparatory ecosystems for AI-augmented work and organizations as ongoing learning environments. Sustainability is addressed through feedback loops that allow learning content, AI models, and human practices to co-evolve over time, supporting lifelong learning and workforce resilience.

This study contributes to learning and technology research by offering an interdisciplinary perspective that integrates AI, learning sciences, and organizational behavior. It provides educators, learning designers, and organizational leaders with a conceptual foundation for designing future-ready learning experiences that are adaptive, inclusive, and aligned with the realities of human-AI collaboration in contemporary digital workplaces.

Nurturing New Teachers: Connecting School Mentors through Online Collaboration

Jenny Fogarty and Leanne Gray, Ed.D., Anglia Ruskin University, Essex, United Kingdom

Training to become a teacher in England has undergone significant transformation in recent years resulting in changes across the country to how teacher training is delivered (Fogarty and Gray, 2024). This research provides an overview of the approach taken by one Higher Education Institution to create a Community of Practice (Lave and Wenger, 1991) to provide situated learning for professionals in their workplace, as they support the next generation of teachers in their profession.

All trainee teachers in England are required to spend 120 days in a school setting under the expert guidance and supervision of an experienced teacher: their School Mentor. Providing the necessary support to these mentors is a challenge for all teacher training providers, particularly as the School Mentor role is voluntary and there is no statutory requirement by schools in England to provide trainee teachers with a placement. The role of the provider in quality assuring…

Keywords: Professional Learning, Collaboration, Community of Practice

Nurturing New Teachers: Connecting School Mentors through Online Collaboration

Jenny Fogarty amd Leanne Gray, Ed.D.

Training to become a teacher in England has undergone significant transformation in recent years resulting in changes across the country to how teacher training is delivered (Fogarty and Gray, 2024). This research provides an overview of the approach taken by one Higher Education Institution to create a Community of Practice (Lave and Wenger, 1991) to provide situated learning for professionals in their workplace, as they support the next generation of teachers in their profession.

All trainee teachers in England are required to spend 120 days in a school setting under the expert guidance and supervision of an experienced teacher: their School Mentor. Providing the necessary support to these mentors is a challenge for all teacher training providers, particularly as the School Mentor role is voluntary and there is no statutory requirement by schools in England to provide trainee teachers with a placement. The role of the provider in quality assuring the mentoring provision straddles regulatory requirements, professional development and a wide variety of mentor prior experiences and this research has been developed to best answer the question: "How can a Community of Practice approach support School Mentors who mentor trainee teachers during their professional placement?"

Presented using case study methodology, the research showcases how the University tackled the challenge of providing high quality professional development and training to School Mentors responsible for their trainee teachers' classroom experience. It will showcase the rationale for the approaches taken including combining a range of strategies to meet their needs: the development of a Mentor Hub, online asynchronous resources to support professional development and live online sessions to provide bespoke mentoring advice and guidance (Gray, 2025). Drawing on direct feedback from the School Mentors themselves during the Summer 2025 and Spring 2026 placement experiences the authors argue for a blended approach to collaboration, drawing on School Mentors' expertise and motivation, within a clear framework of expectations for excellence in delivery of mentoring.

IGIP SESSION

Pedagogical Design Guidelines of a Self-Regulated Smart Learning Environment to Support Online Learning Experiences: A Case Study in Adamawa State University, Mubi

Yusufu Gambo, Ph.D., Adamawa State University Mubi, Adamawa, Nigeria

The developments in smart and mobile technologies impact the design of a smart computing environment to support diverse aspects of life. These technologies can be used to support the development of a self-regulated smart learning environment to support online learning systems. Several research works have developed design guidelines for open learning environments; however, most of them were not validated and lack a well-documented process. This paper developed and validated the design guidelines of a self-regulated smart learning environment. The research used mixed methods employing interviews and surveys to explore and examine the level of agreement among experts across departments of Computer Science, Education, and of Directorate of ICT at a University in Nigeria. The analyses used both thematic and descriptive statistics to explore respondents’ opinions and level of agreement on the design guidelines…

Keywords: Self-Regulated Learning, Smart Learning Environment, Pedagogy, Design Guideline, Online Open Education

Pedagogical Design Guidelines of a Self-Regulated Smart Learning Environment to Support Online Learning Experiences: A Case Study in Adamawa State University, Mubi

Yusufu Gambo, Ph.D.

The developments in smart and mobile technologies impact the design of a smart computing environment to support diverse aspects of life. These technologies can be used to support the development of a self-regulated smart learning environment to support online learning systems. Several research works have developed design guidelines for open learning environments; however, most of them were not validated and lack a well-documented process. This paper developed and validated the design guidelines of a self-regulated smart learning environment. The research used mixed methods employing interviews and surveys to explore and examine the level of agreement among experts across departments of Computer Science, Education, and of Directorate of ICT at a University in Nigeria. The analyses used both thematic and descriptive statistics to explore respondents’ opinions and level of agreement on the design guidelines.

The findings revealed strong agreement among participants regarding the proposed design guidelines. These include creating awareness and obtaining institutional support, reviewing learning contexts and objectives, evaluating skills and available technologies, establishing development strategies, reviewing and developing implementation strategies, implementing course contents in the application, and evaluating system functionalities and impacts on the learning process. The findings provided insights into effective strategies for designing and implementing self-regulated smart learning environments in online education systems. Further research is recommended within large contextual settings to enrich the pedagogical design guidelines to inform future design strategies.

Quantitative Analysis of the Sustainability of Consumer Decision-Making through Human-AI Collaboration: An Experiential Assignment

Subhadra Ganguli, Ph.D.,Penn State University Lehigh Valley, Center Valley, Pennsylvania, USA

Consumer decision making in microeconomics is grounded in the principle of maximizing satisfaction subject to budget constraints. This study examines how human–AI collaboration influences such decision processes in an educational setting. Using a two-stage assignment administered to approximately 100 undergraduate students across the 2024–25 academic year, the research first requires students to independently make purchase decisions within fixed income and budget limitations. In the second stage, students apply prompt-engineering strategies to collaborate with a Large Language Model (LLM), re-evaluating their choices under identical budget constraints. Students further explore decision making through two perspectives: that of a typical consumer and that of a dietician-nutritionist, allowing for sustainability-oriented comparisons…

Keywords: Sustainable Consumer Behavior, Prompt Engineering LLMs, Microeconomics, Budget Constraint

Quantitative Analysis of the Sustainability of Consumer Decision-Making through Human-AI Collaboration: An Experiential Assignment

Subhadra Ganguli, Ph.D.

Consumer decision making in microeconomics is grounded in the principle of maximizing satisfaction subject to budget constraints. This study examines how human–AI collaboration influences such decision processes in an educational setting. Using a two-stage assignment administered to approximately 100 undergraduate students across the 2024–25 academic year, the research first requires students to independently make purchase decisions within fixed income and budget limitations. In the second stage, students apply prompt-engineering strategies to collaborate with a Large Language Model (LLM), re-evaluating their choices under identical budget constraints. Students further explore decision making through two perspectives: that of a typical consumer and that of a dietician-nutritionist, allowing for sustainability-oriented comparisons.

This study investigates whether structured human–AI collaboration—particularly role-based prompting—can support more sustainable consumer decision making. Quantitative and qualitative analyses of student submissions reveal how LLM-assisted reasoning differs from, complements, or improves upon students’ initial choices. The findings contribute to understanding the potential of AI-augmented decision tools in microeconomics education and sustainable consumption behavior.

IGIP SESSION

Nimbus: An Industry-Aligned Learning Studio

Smaranjit Ghose, Ph.D. and Geetha Prakash, Ph.D., byteXL TechEd Private Limited, Telangana, India

As industry hiring expectations increasingly emphasize system thinking, production readiness, and real-world project execution, computing education must evolve beyond isolated problem-solving exercises. This paper presents a real-world case study of a proprietary learning portal, Nimbus, which operationalizes this shift through an integrated ecosystem for content authoring, delivery, assessment, and analytics. The platform is currently deployed across 100+ industry-aligned courses, serves over 150,000 learners across multiple partner institutions, supports an average of 250 assessments per month, more than a lakh project submissions on the Nimbus Workspace.

Innovation in the platform lies in its structured yet flexible content-creation framework, built around hierarchical course design, reusable learning components, version control, and controlled import–export workflows. Levels of abstraction are calibrated to help learners assemble functional systems using frameworks such as PyTorch, while remaining anchored in core concepts, ensuring that theoretical knowledge is consistently experienced in production-like contexts…

Keywords: Learning Platforms, Content Authoring, Project-based Assessment, Bloom's Taxonomy, Coding Challenges

Nimbus: An Industry-Aligned Learning Studio

Smaranjit Ghose, Ph.D. and Geetha Prakash, Ph.D.

As industry hiring expectations increasingly emphasize system thinking, production readiness, and real-world project execution, computing education must evolve beyond isolated problem-solving exercises. This paper presents a real-world case study of a proprietary learning portal, Nimbus, which operationalizes this shift through an integrated ecosystem for content authoring, delivery, assessment, and analytics. The platform is currently deployed across 100+ industry-aligned courses, serves over 150,000 learners across multiple partner institutions, supports an average of 250 assessments per month, more than a lakh project submissions on the Nimbus Workspace.

Innovation in the platform lies in its structured yet flexible content-creation framework, built around hierarchical course design, reusable learning components, version control, and controlled import–export workflows. Levels of abstraction are calibrated to help learners assemble functional systems using frameworks such as PyTorch, while remaining anchored in core concepts, ensuring that theoretical knowledge is consistently experienced in production-like contexts. Native multimodal authoring supports embedded and executable code blocks, rich media, dataset-driven walkthroughs, and scenario-based explanations aligned with authentic developer workflows.

A pedagogy-first approach anchors every learning object and assessment to workplace-realistic scenarios, including environment management, production constraints, and subject progression across parallel and subsequent courses. Assessment design spans scenario-based MCQs mapped across Bloom’s Taxonomy, coding tasks, and use-case questions with randomized variables. Auto-graded programming evaluations integrate with GitHub and SonarQube to assess functional correctness, code quality, and testing rigor.

Project evaluation is powered by Nimbus, a proprietary rubric-based submission and grading platform that evaluates learners across code quality, functionality, testing, originality, and technical articulation, translating outcomes into transparent grade bands. Collaborative authoring and review workflows are supported through role-based access, approval pipelines, and lifecycle tracking integrated with ClickUp.

Impact is further amplified through comprehensive analytics dashboards that provide student reports, leaderboards, coding challenge results, and week-wise and month-wise performance insights at cohort and institutional levels. Together, these capabilities demonstrate how tightly coupled pedagogy, authoring tools, and evaluation platforms can deliver scalable, authentic, and industry-aligned learning—addressing both current workforce demands and future educational innovation.

BreakThrough Communication+: Transform Learning From Knowledge to Lived Practice

Susan Glaser, Ph.D., and Peter Glaser, Ph.D., Glaser & Associates, Inc., Eugene, Oregon, USA

BreakThrough Conflict+ is our evidence-based blended learning curriculum that helps people move independently from knowing communication skills to actually living them in real conversations through innovative learning experiences designed for lifelong learning. These pre/post measures add objective impact evidence alongside completion, badges, and progress tracking—showing not just what learners finished, but what they feel prepared to do differently—supported by evaluation/learning analytics from pre-post surveys. Built on over 35 years of our published research, this new program structures learning into clearly defined tiers each with real-world assignments, self-reflection, and measurable progress, grounded in collaborative learning methodologies. Participants proceed at their own pace and begin, in tier 1 with a thorough foundational knowledge check. They then progress through applied practice in each of the core action learning competencies, with action practice to turn learning into mastery. Advanced tiers focus on leaders who facilitate team practice, design recurring communication rituals, and cultivate a culture of psychological safety and accountability…

Keywords: Hybrid Learning, Experiential Learning, Blended Learning, Communication Skills, Micro-Learning

BreakThrough Communication+: Transform Learning From Knowledge to Lived Practice

Susan Glaser, Ph.D., and Peter Glaser, Ph.D.

BreakThrough Conflict+ is our evidence-based blended learning curriculum that helps people move independently from knowing communication skills to actually living them in real conversations through innovative learning experiences designed for lifelong learning. These pre/post measures add objective impact evidence alongside completion, badges, and progress tracking—showing not just what learners finished, but what they feel prepared to do differently—supported by evaluation/learning analytics from pre-post surveys. Built on over 35 years of our published research, this new program structures learning into clearly defined tiers each with real-world assignments, self-reflection, and measurable progress, grounded in collaborative learning methodologies. Participants proceed at their own pace and begin, in tier 1 with a thorough foundational knowledge check. They then progress through applied practice in each of the core action learning competencies, with action practice to turn learning into mastery. Advanced tiers focus on leaders who facilitate team practice, design recurring communication rituals, and cultivate a culture of psychological safety and accountability.

The BTC+ learning system integrates self-paced micro-learning, knowledge quizzes, reflective worksheets, and leader-led application, so learners engage actively in turning knowledge into applied behavior. Badges and certificates mark each milestone, giving organizations a scalable way to document learning and track progress over time. Learners receive customized, confidential feedback on each assignment while their leaders receive usage data pinpointing participant progress.

This session will demonstrate how the BTC+ design hardwires skills into daily behavior and team routines in a participatory/hands-on session. Participants will see sample assignments and explore how micro-learning and leadership practice sustain change.

Links:

Evidence-based (pre-post surveys result): https://www.canva.com/design/DAGi87WKyIg/W5JP4tElW5NG3fa20y1e6g/view?utm_content=DAGi87WKyIg&utm_campaign=designshare&utm_medium=link2&utm_source=uniquelinks&utlId=h148608cd7c

Published Research: https://www.theglasers.com/results.html

Civic Learning Without Limits: The National Mall Experience Anywhere

Jeremy Goldstein, Trust for the National Mall, Washington, D.C., USA

As the nation commemorates its 250th anniversary in 2026, the National Park Service and the Trust for the National Mall have partnered to create an innovative tool for educators, students, and visitors: the National Mall Gateway. This digital platform offers unprecedented access to images, content, and civic learning resources from the nation's "Front Yard," serving both onsite and virtual audiences. Beginning with a survey of research on public lands and civic education, this session explores the challenges and opportunities educators face in today's evolving civic learning landscape.

Keywords: Civics, K-12, Public Lands, Civic Learning

Civic Learning Without Limits: The National Mall Experience Anywhere

Jeremy Goldstein

As the nation commemorates its 250th anniversary in 2026, the National Park Service and the Trust for the National Mall have partnered to create an innovative tool for educators, students, and visitors: the National Mall Gateway. This digital platform offers unprecedented access to images, content, and civic learning resources from the nation's "Front Yard," serving both onsite and virtual audiences. Beginning with a survey of research on public lands and civic education, this session explores the challenges and opportunities educators face in today's evolving civic learning landscape.

From Prediction to Action: Artificial Intelligence for Equitable Allocation of Student Support Resources

Alejandra González, Ph.D., David Barrera, Ph.D., and Jorge Marquez, Pontificia Universidad Javeriana, Distrito Capital, Colombia

Improving student success has become a central priority for higher education institutions worldwide. While many universities deploy academic support, their effectiveness depends heavily on aligning interventions with the diverse needs, and performance trajectories of students. A key question therefore emerges. Which interventions should be available for which students? This requires moving beyond uniform support schemes and instead adopting data-informed strategies that acknowledge heterogeneity in academic performance, learning behaviors, socioeconomic constraints, health conditions, and access to educational resources. Since offering all types of support to all students is neither operationally feasible nor cost-effective, institutions must advance toward targeted, equitable, and impact-oriented allocation of resources.

This work synthesizes findings from three applied research projects in learning analytics and AI conducted at two high-quality Colombian universities: Pontificia Universidad Javeriana and the Universidad Nacional de Colombia. Our work moves beyond traditional predictive models focused solely on classifying students by dropout risk…

Keywords: Student Support Resources, Artificial Intelligence, Dropout

From Prediction to Action: Artificial Intelligence for Equitable Allocation of Student Support Resources

Alejandra González, Ph.D., David Barrera, Ph.D., and Jorge Marquez

Improving student success has become a central priority for higher education institutions worldwide. While many universities deploy academic support, their effectiveness depends heavily on aligning interventions with the diverse needs, and performance trajectories of students. A key question therefore emerges, Which interventions should be available for which students? This requires moving beyond uniform support schemes and instead adopting data-informed strategies that acknowledge heterogeneity in academic performance, learning behaviors, socioeconomic constraints, health conditions, and access to educational resources. Since offering all types of support to all students is neither operationally feasible nor cost-effective, institutions must advance toward targeted, equitable, and impact-oriented allocation of resources.

This work synthesizes findings from three applied research projects in learning analytics and AI conducted at two high-quality Colombian universities: Pontificia Universidad Javeriana and the Universidad Nacional de Colombia. Our work moves beyond traditional predictive models focused solely on classifying students by dropout risk. Instead, we address a less-explored challenge: how to use AI to not only predict, but also to optimize, prioritize, and justify the allocation of academic interventions in order to maximize institutional impact.

Across the studies, we developed advanced dropout prediction models integrated into an institutional early-warning system, as well as subject-specific risk models for critical engineering courses. We incorporated supervised and unsupervised machine-learning methods to characterize differentiated risk profiles and vulnerability patterns. Importantly, all predictive components were complemented with fairness audits and counterfactual analyses that estimate which academic, institutional conditions could realistically shift a student from a high-risk to a lower-risk trajectory.

By integrating these insights, we propose a decision-making framework to prioritize the strategic allocation of support resources. We argue that true innovation lies not merely in predicting dropout, but in using AI-generated evidence to redesign institutional support systems, mitigate bias, and assign interventions where they are most needed and most effective.

Advancing Online Learning: A Strategic Approach to Quality and Innovation

Sara Gretencord, Purdue University, Fowler, Indiana, USA

This presentation aims to provide insight into Purdue University Online’s (PUO) structured approach to quality assurance (QA) for both credit and noncredit courses. By showcasing the standard operating procedure (SOP) established for data intake from course review requests and IDATA lists, this session will emphasize best practices for ensuring high-quality online education.

In an era where online learning plays a pivotal role in higher education, maintaining course quality is crucial. The PUO QA team has developed a comprehensive SOP that ensures a consistent, organized, and efficient method for obtaining course review requests, processing semester course lists, and managing faculty approvals. This process enables academic institutions to enhance the quality of their digital learning environments while fostering student success.

Keywords: Quality Assurance, Innovation

Advancing Online Learning: A Strategic Approach to Quality and Innovation

Sara Gretencord

This presentation aims to provide insight into Purdue University Online’s (PUO) structured approach to quality assurance (QA) for both credit and noncredit courses. By showcasing the standard operating procedure (SOP) established for data intake from course review requests and IDATA lists, this session will emphasize best practices for ensuring high-quality online education.

In an era where online learning plays a pivotal role in higher education, maintaining course quality is crucial. The PUO QA team has developed a comprehensive SOP that ensures a consistent, organized, and efficient method for obtaining course review requests, processing semester course lists, and managing faculty approvals. This process enables academic institutions to enhance the quality of their digital learning environments while fostering student success.

The Forgotten Sense in Adult Learning: Vestibular Function in Digital Work and Education

Constance Hamner Campbell, Pamela McCray, Ph.D., and Norman St. Clair, Ph.D., University of the Incarnate Word, San Antonio, Texas, USA

Digital environments have fundamentally reshaped adult work and learning, offering unprecedented access, flexibility, and innovation across professional, higher education, and lifelong learning contexts. However, these environments also represent a significant shift in how adult learners physically engage with learning tasks and their surrounding environments. Emerging evidence suggests that reduced vestibular input—a consequence of prolonged, screen-based engagement—may contribute to challenges in balance, visuomotor coordination, attention, anxiety, reading efficiency, and information processing. These factors are closely tied to core adult learning outcomes, including cognitive endurance, self-regulation, and sustained attention, all of which directly affect learning effectiveness and workplace performance. This paper examines the physiological implications of increasingly screen-dependent work and learning environments, with a specific focus on the vestibular system—a foundational sensory system that is primarily activated by head movement and spatial orientation…

Keywords: Vestibular System, Adult Learning, Digital Learning Environments, Cognitive Performance, Screen-Based Learning

The Forgotten Sense in Adult Learning: Vestibular Function in Digital Work and Education

Constance Hamner Campbell, Pamela McCray, Ph.D., and Norman St. Clair, Ph.D.

Digital environments have fundamentally reshaped adult work and learning, offering unprecedented access, flexibility, and innovation across professional, higher education, and lifelong learning contexts. However, these environments also represent a significant shift in how adult learners physically engage with learning tasks and their surrounding environments. Emerging evidence suggests that reduced vestibular input—a consequence of prolonged, screen-based engagement—may contribute to challenges in balance, visuomotor coordination, attention, anxiety, reading efficiency, and information processing. These factors are closely tied to core adult learning outcomes, including cognitive endurance, self-regulation, and sustained attention, all of which directly affect learning effectiveness and workplace performance. This paper examines the physiological implications of increasingly screen-dependent work and learning environments, with a specific focus on the vestibular system—a foundational sensory system that is primarily activated by head movement and spatial orientation. Screen-based adult learning and knowledge work typically require extended periods of head fixation and sedentary posture, significantly reducing the range and frequency of vestibular stimulation. Drawing on interdisciplinary research from neuroscience, adult education, and developmental psychology, this reduction may affect not only physical well-being but also cognitive processes essential to complex problem-solving, learning transfer, and professional decision-making. By reframing adult learning through an embodied and physiological lens, this work examines holistic approaches to instructional design, faculty practice, and organizational learning structures to support adult learners’ cognitive resilience, engagement, and long-term functional performance in increasingly digital educational and workplace environments.

Better Learning by Design: Applying AI + Human Insight in Learning Experience Design

Jessica Hecht and Tiffany Chapman, Ph.D., Six Red Marbles, Needham, Massachusetts, USA

As AI adoption accelerates across higher education and workplace learning, institutions face a familiar tension: How can we leverage AI for efficiency without compromising academic integrity, equity, or instructional quality? This session delivers best practice design considerations for higher ed decision makers as well as instructional design professionals.

Presenters will demonstrate a practical AI + HI Workflow: a balanced approach that positions AI as an accelerator for ideation, drafting, and consistency checks while ensuring that human expertise drives pedagogy, accessibility, context, and ethical judgment. Participants will explore short case vignettes showing how instructional designers and faculty have tested AI responsibly in real course development projects, with lessons learned along the way…

Keywords: AI in Education, Course Design, Learning Experience Design, AI in Course Development

Better Learning by Design: Applying AI + Human Insight in Learning Experience Design

Jessica Hecht and Tiffany Chapman, Ph.D.

As AI adoption accelerates across higher education and workplace learning, institutions face a familiar tension: How can we leverage AI for efficiency without compromising academic integrity, equity, or instructional quality? This session delivers best practice design considerations for higher ed decision makers as well as instructional design professionals.

Presenters will demonstrate a practical AI + HI Workflow: a balanced approach that positions AI as an accelerator for ideation, drafting, and consistency checks while ensuring that human expertise drives pedagogy, accessibility, context, and ethical judgment. Participants will explore short case vignettes showing how instructional designers and faculty have tested AI responsibly in real course development projects, with lessons learned along the way.

The second half of the session includes a participatory exercise using the AI + HI Touchpoint Canvas, a tool adapted from our workflow model. This interactive activity guides attendees to analyze where AI can meaningfully support their own design processes and where human oversight is essential to protect learning quality.

Participants will leave with: -A proven, human-centered workflow for combining AI and instructional expertise -Practical examples of AI use in course design that preserve rigor and equity -A tool they can take back to their teams to begin or refine their own AI + HI practices

This session is ideal for instructional designers, L&D professionals, faculty developers, and academic leaders seeking applied, ethical, and scalable approaches to AI in learning design.

AI Playbooks for Education Leaders: Work Faster, Work Smarter, Communicate with Impact

Yukima Hughes, Beyond the Classroom Consulting, LLC, South Plainfield, New Jersey, USA

Schools and small teams need fast, reliable ways to use AI without chaos or compliance risk. This session shows a practical playbook you can apply immediately to planning, meeting agendas, family communications, data-to-decisions, and everyday writing. We’ll frame AI as a workflow, not a magic trick: clear inputs, quality checks, and guardrails that match your policies. You will see live prompts and side-by-side outputs, then practice a simple prompt pattern that produces consistent results across roles. We will map three high-leverage use cases that typically save 3–5 hours per week and review a lightweight privacy checklist you can adapt. You will leave with a one-page workflow template, a starter prompt pack for instructional leadership and curriculum tasks, and a quick rubric for evaluating AI outputs. The aim is confidence and repeatability so teams work faster, work smarter, and communicate with impact without adding tools or stress. Session fits K–12 leaders, coaches, faculty, and small business education teams; it also translates well to higher ed centers for teaching and learning.

Keywords: AI in Education, Instructional Leadership, Prompt Engineering, Curriculum and Literacy, Workflow Design

AI Playbooks for Education Leaders: Work Faster, Work Smarter, Communicate with Impact

Yukima Hughes

Schools and small teams need fast, reliable ways to use AI without chaos or compliance risk. This session shows a practical playbook you can apply immediately to planning, meeting agendas, family communications, data-to-decisions, and everyday writing. We’ll frame AI as a workflow, not a magic trick: clear inputs, quality checks, and guardrails that match your policies. You will see live prompts and side-by-side outputs, then practice a simple prompt pattern that produces consistent results across roles. We will map three high-leverage use cases that typically save 3–5 hours per week and review a lightweight privacy checklist you can adapt. You will leave with a one-page workflow template, a starter prompt pack for instructional leadership and curriculum tasks, and a quick rubric for evaluating AI outputs. The aim is confidence and repeatability so teams work faster, work smarter, and communicate with impact without adding tools or stress. Session fits K–12 leaders, coaches, faculty, and small business education teams; it also translates well to higher ed centers for teaching and learning.

 

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