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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Science Fiction for Today: The Primer

Alicia Sanchez, Ph.D., MPF, Sanford, Florida, USA

In Neal Stephenson’s Diamond Age, the “Primer” is an interactive book that teaches a child how to think, adapt, and navigate a complex world. This session presents a real world revival of that idea for adult knowledge workers, using current AI capabilities instead of science fiction.

The Primer is an AI-powered learning companion that lives inside email and the Microsoft 365 suite. It uses your existing tools, content, and policies to turn everyday work into structured practice. AI generates realistic scenarios, scaffolds complexity from simple tasks to messy multi-stakeholder problems, answers user questions in context, grades assignments against clear rubrics, and even plays virtual team roles such as client, reviewer, or regulator…

Keywords: AI-Powered Learning, Experiential Training, Scaffolding, Complexity, Sci Fi

Science Fiction for Today: The Primer

Alicia Sanchez, Ph.D.

In Neal Stephenson’s Diamond Age, the “Primer” is an interactive book that teaches a child how to think, adapt, and navigate a complex world. This session presents a real world revival of that idea for adult knowledge workers, using current AI capabilities instead of science fiction.

The Primer is an AI-powered learning companion that lives inside email and the Microsoft 365 suite. It uses your existing tools, content, and policies to turn everyday work into structured practice. AI generates realistic scenarios, scaffolds complexity from simple tasks to messy multi-stakeholder problems, answers user questions in context, grades assignments against clear rubrics, and even plays virtual team roles such as client, reviewer, or regulator.

Attendees will see how this pattern can support legal, finance, healthcare, engineering, and consulting teams without building a new LMS. The talk will cover the instructional design backbone, including scaffolding, feedback, and telemetry, and the technical building blocks, including LLMs with retrieval and deep integration with Outlook and 365 apps.

Participants will leave with a concrete blueprint for creating their own “Primer” style companion that treats learning as part of daily work, not a separate event, and raises the quality and speed of decisions across their organization.

IGIP SESSION

Auditing Commercial AI IELTS (International English Language Testing System) Writing Apps for Self-Directed Learners

Rohib Sangia, University of Aberdeen, Scotland, United Kingdom

Within IELTS preparation markets, commercial AI-based writing evaluators now operate as low-cost, constantly available tools that advertise an ability to approximate human scoring practices grounded in established writing assessment criteria. Services such as Cathoven, Lexibot and Engnovate position themselves as comprehensive resources for self-directed IELTS writing studies, targeting candidates who have limited, irregular, or no access to sustained teacher feedback. This study evaluates whether commercial AI-based IELTS writing checkers can operate as credible proxies for human scoring in self-access preparation. Focusing on Cathoven, Lexibot and Engnovate, it addresses two questions: the extent to which their scores align with official IELTS bands across tasks and levels, and the pedagogical and construct-related quality of the feedback they provide. The study is grounded in contemporary language testing theory and debates on…

Keywords: IELTS Writing, AI-Based Assessment, Automated Scoring, Feedback Quality, Bayesian Modelling

Auditing Commercial AI IELTS (International English Language Testing System) Writing Apps for Self-Directed Learners

Rohib Sangia

Within IELTS preparation markets, commercial AI-based writing evaluators now operate as low-cost, constantly available tools that advertise an ability to approximate human scoring practices grounded in established writing assessment criteria. Services such as Cathoven, Lexibot and Engnovate position themselves as comprehensive resources for self-directed IELTS writing studies, targeting candidates who have limited, irregular, or no access to sustained teacher feedback. This study evaluates whether commercial AI-based IELTS writing checkers can operate as credible proxies for human scoring in self-access preparation. Focusing on Cathoven, Lexibot and Engnovate, it addresses two questions: the extent to which their scores align with official IELTS bands across tasks and levels, and the pedagogical and construct-related quality of the feedback they provide. The study is grounded in contemporary language testing theory and debates on construct representation, rating scale use and washbacks. The dataset comprises 110 Task 1 and Task 2 scripts from official IELTS materials, each with benchmark human scores and commentary. Quantitative analyses will examine agreement patterns and systematic bias using descriptive statistics, classical agreement indices, and a Bayesian latent variable model estimating platform-specific severity, leniency, and error variance. Qualitative deductive thematic analysis, validated by expert raters, will map feedback to the four IELTS criteria, assessing specificity, internal consistency and formulaicity. Integrated findings will characterize the reliability and affordances of each system, informing teacher advice, procurement decisions and policy debates on the role of commercial AI raters in high-stakes assessment.

Transferring AI Knowledge from Learning Environments into R&D and Innovation Spaces

Christian-Andreas Schumann, Ph.D., Emelie Schwill and Xiaoli Chen, Ph.D., West Saxon University of Zwickau, Saxony, Germany

Learning environments are places, spaces or platforms where people can acquire knowledge. Education and training, especially at universities in the field of knowledge management as a socio-organizational discipline, has always been based on technical and formal aspects of computer science and mathematics, including artificial intelligence.

Both sides create a hybrid dimension in which a problem-solving symbiosis arises between pure knowledge automation, including corresponding tools, and the design of knowledge processes within the framework of organizational intelligence. Even without explicit mention of AI, it has been an implicit subject of research and teaching in the context of knowledge management for decades. Learning environments form the framework for AI knowledge transfer and AI knowledge representation…

Keywords: Transferring AI Knowledge, R&D Space, Innovation Space, Systematic Transformation of AI-Knowledge

Transferring AI Knowledge from Learning Environments into R&D and Innovation Spaces

Christian-Andreas Schumann, Ph.D., Emelie Schwill and Xiaoli Chen, Ph.D.

Learning environments are places, spaces or platforms where people can acquire knowledge. Education and training, especially at universities in the field of knowledge management as a socio-organizational discipline, has always been based on technical and formal aspects of computer science and mathematics, including artificial intelligence.

Both sides create a hybrid dimension in which a problem-solving symbiosis arises between pure knowledge automation, including corresponding tools, and the design of knowledge processes within the framework of organizational intelligence. Even without explicit mention of AI, it has been an implicit subject of research and teaching in the context of knowledge management for decades. Learning environments form the framework for AI knowledge transfer and AI knowledge representation.

However, research, development and innovation are based on competencies and skills as well as on real, problem-oriented and practical contexts. The necessary transformation of knowledge into skills and innovations is seriously limited in pure learning environments. For this reason, a transition to other spaces is necessary, initially to research, development and innovation spaces, supplemented by spaces for collaboration and implementation.

AI knowledge is analyzed and operationalized in research and development. Knowledge-based models are transformed and embedded in R&D processes. The interaction between AI knowledge and R&D space also permanently changes the latter. The interaction between humans and AI leads to hybridization. Knowledge becomes competence.

The subsequent transfer of AI knowledge and AI capabilities to innovation spaces initiates further transformations through new combinations and implementation possibilities. AI knowledge becomes application-oriented by bringing together the creative application of human and natural intelligence. The result is new knowledge-based products and services that have a direct influence on market design and development through external communication and use.

This paper presents the systematic transformation of AI knowledge into logically connected spaces, as well as several case studies describing the epistemic process of transferring AI knowledge from the learning environment to the fields of research, development and innovation.

New Directions in Assessment: Societal Impact and Artificial Intelligence

Kent Seaver, University of Texas at Dallas-Naveen Jindal School of Management, Richardson, Texas, USA

Artificial Intelligence is one of the most talked about phrases in higher education. From an assessment standpoint, what can be achieved by implementing AI into assessment? In this session, we will learn why the need for indirect measures is more important now than ever. The impact of using this approach inside the classroom, as well as your college in general, can be felt for years to come and can help shape policy decisions that impact all educational stakeholders. In addition, we will also examine how AI and indirect measures can be used to assist student organizations in gaining a better understanding of the needs of the communities in which they live, work, and serve. The session will conclude by sharing ideas on how to create and use data visualizations to document and tell the assessment story. The presentation will explain how indirect measures are an important part of assessing learning…

Keywords: Artificial Intelligence, Indirect Measures, Society

New Directions in Assessment: Societal Impact and Artificial Intelligence

Kent Seaver

Artificial Intelligence is one of the most talked about phrases in higher education. From an assessment standpoint, what can be achieved by implementing AI into assessment? In this session, we will learn why the need for indirect measures is more important now than ever. The impact of using this approach inside the classroom, as well as your college in general, can be felt for years to come and can help shape policy decisions that impact all educational stakeholders. In addition, we will also examine how AI and indirect measures can be used to assist student organizations in gaining a better understanding of the needs of the communities in which they live, work, and serve. The session will conclude by sharing ideas on how to create and use data visualizations to document and tell the assessment story. The presentation will explain how indirect measures are an important part of assessing learning. That will be followed by a discussion of how AI is impacting the assessment community, and how that can be aligned to meet the evolving needs of academia and society. AI and indirect measures in assessment is a new phenomenon, and one that will be discussed in terms of data collection. Information on what and how data is collected data, along with relevant examples of how the data is shared with key stakeholders will be described. Learn about one institution’s unique approach that goes beyond the classroom and traditional metrics, while integrating an AI lens to assessment.

Educational Models and Security Solutions in Ukrainian Universities under Martial Law: A Regional Analysis

Bohdan Shunevych, Ph.D., Stepan Gzhytskyi National University of Veterinary Medicine and Biotechnologies of Lviv, North Campus, Lviv, Ukraine, Mykola Petrovskyi, Ph.D., Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, and Oleksandr Shcherbyna, National Technical University of Ukraine, Kyiv, Ukraine

This study examines how Ukrainian universities have adapted their operations under martial law, comparing responses across Eastern, Central, and Western regions. The fundamental distinction lies in proximity to active combat: Western and Central universities maintain some in-person instruction with shelter protocols and host displaced institutions, while Eastern universities operate almost entirely underground or remotely, having suffered near-total physical destruction, with many relocating entirely to safer regions.

The research examines academic practices in response to wartime constraints, addressing three key questions: What structural and pedagogical adaptations have universities implemented to sustain teaching and research? How have teaching staff and students adapted under extreme uncertainty? What lessons can enhance educational resilience in future crises?

Using comparative case study methodology, the research analyzes organizational and pedagogical transformations in higher education institutions across Ukraine's three regions during the ongoing war…

Keywords: Educational Process Organization, Innovative Technologies, Artificial Intelligence, Martial Law, Academic Resilience

Educational Models and Security Solutions in Ukrainian Universities under Martial Law: A Regional Analysis

Bohdan Shunevych, Ph.D., Mykola Petrovskyi, Ph.D., and Oleksandr Shcherbyna

This study examines how Ukrainian universities have adapted their operations under martial law, comparing responses across Eastern, Central, and Western regions. The fundamental distinction lies in proximity to active combat: Western and Central universities maintain some in-person instruction with shelter protocols and host displaced institutions, while Eastern universities operate almost entirely underground or remotely, having suffered near-total physical destruction, with many relocating entirely to safer regions.

The research examines academic practices in response to wartime constraints, addressing three key questions: What structural and pedagogical adaptations have universities implemented to sustain teaching and research? How have teaching staff and students adapted under extreme uncertainty? What lessons can enhance educational resilience in future crises?

Using comparative case study methodology, the research analyzes organizational and pedagogical transformations in higher education institutions across Ukraine's three regions during the ongoing war. Data sources include institutional documents, interviews with administrators and faculty, and analysis of implemented policies regarding distance learning, security protocols, and curriculum modifications.

Universities implemented rapid transitions to distance and blended learning using platforms such as Moodle, Zoom, and Microsoft Teams, though the feasibility of in-person education varied by proximity to conflict zones. Institutions adopted decentralized administrative structures for greater flexibility and introduced curricula addressing wartime realities—including conflict resolution, cybersecurity, and emergency management. Mental health services and resilience training became strategic priorities. International collaboration provided critical access to mobility programs, research funding, and academic solidarity.

Three distinct regional models emerged: Eastern Ukraine exemplifies survival-driven innovation through underground and relocated education; Central Ukraine represents hybrid resilience, supporting both local operations and displaced institutions; Western Ukraine embodies resilient continuity, sustaining academic traditions and international engagement. Together, these regions form a national ecosystem of educational resilience, offering lessons for global educational preparedness in future crises.

AI Agents and Digital Adoption Platforms: Driving Continuous Learning, AI Skill Development, and Real-Time Performance Support

Harvey Singh, Instancy, Inc., Durham, North Carolina, USA

Discover how AI agents and Digital Adoption Platforms (DAPs) revolutionize organizational learning by delivering proactive performance support and continuous AI skill development.

Traditional training fails to match AI's rapid evolution—long courses become obsolete, leaving employees unable to apply skills in real time. AI agents provide just-in-time guidance, prompts, and shared knowledge exactly while using AI tools or performing tasks in workflows, while DAPs embed "see it, try it, test it" experiences directly into daily operations.shrm.

Teams co-create social, bite-sized content like guides, simulations, and interactive videos, fostering ongoing upskilling without disrupting work. Performance support ensures contextual prompts and collaborative knowledge sharing boost retention, productivity, and adaptability in the AI era.

Keywords: Digital Adoption Platform, Performance Support, Multimodal AI Agents

AI Agents and Digital Adoption Platforms: Driving Continuous Learning, AI Skill Development, and Real-Time Performance Support

Harvey Singh

Discover how AI agents and Digital Adoption Platforms (DAPs) revolutionize organizational learning by delivering proactive performance support and continuous AI skill development.

Traditional training fails to match AI's rapid evolution—long courses become obsolete, leaving employees unable to apply skills in real time. AI agents provide just-in-time guidance, prompts, and shared knowledge exactly while using AI tools or performing tasks in workflows, while DAPs embed "see it, try it, test it" experiences directly into daily operations.shrm

Teams co-create social, bite-sized content like guides, simulations, and interactive videos, fostering ongoing upskilling without disrupting work. Performance support ensures contextual prompts and collaborative knowledge sharing boost retention, productivity, and adaptability in the AI era.

Governing Generative Artificial Intelligence (AI) by Preserving Human Creativity in Institutional Systems

Norman St. Clair, Ph.D. and Pamela McCray, Ph.D., Dreeben School of Education, University of the Incarnate Word, San Antonio, Texas, USA

Artificial intelligence has progressed beyond its experimental phase in higher education and now operates as part of the institutional infrastructure, influencing teaching, assessment, and scholarly work. Our presentation uses a meta-synthesis of institutional policy documents, faculty development initiatives, and international AI governance frameworks to examine how universities are responding to the ethical, organizational, and pedagogical realities of Generative AI. The purpose of our research is to develop a conceptually grounded, policy-relevant framework for governing Generative AI as a core institutional system rather than a classroom-level tool. Our guiding research question asks: How can universities ethically govern and integrate Generative AI into their institutional structures while preserving human creativity, transparency, and academic integrity? Drawing on sociotechnical and institutional theory, we frame AI governance not as a matter of compliance but…

Keywords: Generative Artificial Intelligence (AI), Institutional Governance Model, Artificial Intelligence (AI) Ethical Considerations, Higher Education Institutions (HEI)

Governing Generative Artificial Intelligence (AI) by Preserving Human Creativity in Institutional Systems

Norman St. Clair, Ph.D. and Pamela McCray, Ph.D.

Artificial intelligence has progressed beyond its experimental phase in higher education and now operates as part of the institutional infrastructure, influencing teaching, assessment, and scholarly work. Our presentation uses a meta-synthesis of institutional policy documents, faculty development initiatives, and international AI governance frameworks to examine how universities are responding to the ethical, organizational, and pedagogical realities of Generative AI. The purpose of our research is to develop a conceptually grounded, policy-relevant framework for governing Generative AI as a core institutional system rather than a classroom-level tool. Our guiding research question asks: How can universities ethically govern and integrate Generative AI into their institutional structures while preserving human creativity, transparency, and academic integrity? Drawing on sociotechnical and institutional theory, we frame AI governance not as a matter of compliance but as an ongoing negotiation of authority, authorship, and accountability within academic life. We analyze how institutions operationalize broad ethical principles through everyday practices—such as AI literacy initiatives, integrity and authorship policies, curriculum redesign, and assessment reforms. By identifying patterns of alignment, tension, and adaptation across institutions, we propose a governance model that supports responsible innovation while safeguarding the human dimensions of creativity and judgment. Our model offers universities a pathway to integrate AI in ways that strengthen, rather than supplant, the core values and intellectual practices of higher education.

Vibe Coding an AI Tutor

Smruti Sudarshan, LinkedIn Information Technology, Bangalore, India

As generative AI reshapes the learning landscape, educators now have the opportunity to design AI tutors that are not only functional but engaging and learner-centered. This session introduces the concept of vibe coding—a creative, intuitive approach to building AI-powered tutors using large language models (LLMs).

Rather than focusing on technical complexity, vibe coding emphasizes aligning the tone, behavior, and instructional flow of an AI tutor with the learning experience you want to create. We'll explore practical techniques like prompt design, persona crafting, and scaffolding strategies to build tutors that support exploration, autonomy, and motivation.

Participants will leave with a foundational framework and ready-to-use templates for prototyping their own AI tutors—no advanced coding required…

Keywords: Generative AI, AI Tutors, Prompt Engineering, Learning Design, Personalized Learning

Vibe Coding an AI Tutor

Smruti Sudarshan

As generative AI reshapes the learning landscape, educators now have the opportunity to design AI tutors that are not only functional but engaging and learner-centered. This session introduces the concept of vibe coding—a creative, intuitive approach to building AI-powered tutors using large language models (LLMs).

Rather than focusing on technical complexity, vibe coding emphasizes aligning the tone, behavior, and instructional flow of an AI tutor with the learning experience you want to create. We'll explore practical techniques like prompt design, persona crafting, and scaffolding strategies to build tutors that support exploration, autonomy, and motivation.

Participants will leave with a foundational framework and ready-to-use templates for prototyping their own AI tutors—no advanced coding required. Whether you’re an educator, instructional designer, or technologist, this session offers a hands-on introduction to building with AI in ways that are human-centered and pedagogically sound.

Vibe Coding in Action: Designing Learning Apps for Modern L&D

Smruti Sudarshan, LinkedIn Information Technology, Bangalore, India

This session introduces participants to Vibe Coding as a creative and practical approach to building Learning & Development (L&D) applications without deep technical expertise. As L&D professionals increasingly look for faster and more personalized solutions, traditional development methods can feel slow and complex. Vibe Coding focuses on intent, flow, and user experience, enabling educators, trainers, and learning designers to translate learning ideas into functional digital experiences.

In this session, participants will learn how to think like a product designer and use Vibe Coding principles to design and prototype a simple L&D app. The session will walk through identifying a real L&D problem, mapping learner journeys, defining features, and converting these ideas into working app components using low-code or no-code tools. Real examples from L&D use cases such as onboarding, skill development, and continuous learning will be shared…

Keywords: Vibe Coding, Learning & Development, No-Code / Low-Code, Learning Apps, Digital Learning Innovation

Vibe Coding in Action: Designing Learning Apps for Modern L&D

Smruti Sudarshan

This session introduces participants to Vibe Coding as a creative and practical approach to building Learning & Development (L&D) applications without deep technical expertise. As L&D professionals increasingly look for faster and more personalized solutions, traditional development methods can feel slow and complex. Vibe Coding focuses on intent, flow, and user experience, enabling educators, trainers, and learning designers to translate learning ideas into functional digital experiences.

In this session, participants will learn how to think like a product designer and use Vibe Coding principles to design and prototype a simple L&D app. The session will walk through identifying a real L&D problem, mapping learner journeys, defining features, and converting these ideas into working app components using low-code or no-code tools. Real examples from L&D use cases such as onboarding, skill development, and continuous learning will be shared.

The session will include a live demonstration to show how quickly an idea can be turned into a usable learning solution. Participants will also learn best practices, common pitfalls, and ways to iterate based on learner feedback. By the end of the session, attendees will have a clear framework to start building their own L&D apps and experiment with Vibe Coding in their organizations.

This session is ideal for L&D professionals, educators, instructional designers, and anyone interested in blending creativity, technology, and learning innovation.

A Remote Interactive Laboratory for Teaching and Experimentation with Photovoltaic Systems

Dimitar Tokmakov, Ph.D., Slavi Luybomirov, Ph.D., Anna Bekyarova-Tokmakova, Ph.D., and Snezha Shotarova, University of Plovdiv Paisii Hilendarski, Plovdiv, Bulgaria

This paper presents the design and implementation of a remote interactive laboratory aimed at teaching and experimentation with photovoltaic (PV) systems in higher education. The proposed laboratory addresses key limitations of traditional engineering laboratories, such as restricted access, limited availability of equipment, and the need for physical presence. By enabling remote access to a real PV installation, the system supports flexible, hands-on learning experiences for students and researchers regardless of location.

The laboratory integrates IoT-based hardware, including an ESP32 microcontroller, a dual-axis solar tracker, and multiple environmental and electrical sensors, with a web-based learning platform. Real-time data acquisition, visualization, and control are achieved through modern communication technologies such as MQTT, WebSocket, and REST APIs…

Keywords: Remote Laboratory, Photovoltaic Systems Education, Experiential Learning, IoT-Based Learning Environments, Online and Hybrid Engineering Education

A Remote Interactive Laboratory for Teaching and Experimentation with Photovoltaic Systems

Dimitar Tokmakov, Ph.D., Slavi Luybomirov, Ph.D., Anna Bekyarova-Tokmakova, Ph.D., and Snezha Shotarova

This paper presents the design and implementation of a remote interactive laboratory aimed at teaching and experimentation with photovoltaic (PV) systems in higher education. The proposed laboratory addresses key limitations of traditional engineering laboratories, such as restricted access, limited availability of equipment, and the need for physical presence. By enabling remote access to a real PV installation, the system supports flexible, hands-on learning experiences for students and researchers regardless of location.

The laboratory integrates IoT-based hardware, including an ESP32 microcontroller, a dual-axis solar tracker, and multiple environmental and electrical sensors, with a web-based learning platform. Real-time data acquisition, visualization, and control are achieved through modern communication technologies such as MQTT, WebSocket, and REST APIs, while a time-series database and interactive dashboards provide continuous monitoring and analysis. Live video streaming further enhances transparency, safety, and learner engagement by allowing users to visually observe the physical experiment in real time.

Students can remotely perform authentic experiments, such as analyzing the influence of solar irradiance, temperature, and panel orientation on PV performance. Experimental results confirm the reliability of the system and its suitability for educational use. The proposed remote laboratory promotes experiential and participatory learning, supports hybrid and online teaching models, and contributes to innovative learning practices in engineering education. The approach is transferable to other technical disciplines and demonstrates how real-world experimentation can be effectively integrated into digital learning environments.

IGIP SESSION

University Students' Perceptions of the Roles and Uses of Artificial Intelligence (AI) in Statistics Education

Eleni Tsami, University of Piraeus, Piraeus, Greece

This study investigates university students’ perceptions of artificial intelligence (AI) in statistics education, focusing on a sample of 230 students from the Department of Statistics and Insurance Science at the University of Piraeus. Using a structured questionnaire, the research examined students’ familiarity with AI, their attitudes toward its educational use, and their expectations regarding its role in academic and professional contexts. The study's results reveal that while a significant majority of students frequently use AI tools such as ChatGPT, they remain cautious regarding whether ChatGPT could replace traditional instructional methods in higher education. Most students prefer AI as a complementary assistant rather than a replacement for traditional instruction, expressing concerns that it would reduce their need for critical thinking and limit their creative engagement. At the same time, they acknowledge AI’s potential to enhance personalized learning, provide academic support, and strengthen career preparation…

Keywords: Artificial Intelligence, Teaching Statistics, Students

University Students' Perceptions of the Roles and Uses of Artificial Intelligence (AI) in Statistics Education

Eleni Tsami

This study investigates university students’ perceptions of artificial intelligence (AI) in statistics education, focusing on a sample of 230 students from the Department of Statistics and Insurance Science at the University of Piraeus. Using a structured questionnaire, the research examined students’ familiarity with AI, their attitudes toward its educational use, and their expectations regarding its role in academic and professional contexts. The study's results reveal that while a significant majority of students frequently use AI tools such as ChatGPT, they remain cautious regarding whether ChatGPT could replace traditional instructional methods in higher education. Most students prefer AI as a complementary assistant rather than a replacement for traditional instruction, expressing concerns that it would reduce their need for critical thinking and limit their creative engagement. At the same time, they acknowledge AI’s potential to enhance personalized learning, provide academic support, and strengthen career preparation. The findings highlight a critical duality: students recognize the benefits of AI in Statistics education but emphasize the continued necessity of human instruction and pedagogical guidance. The study underscores the importance of balanced integration, combining technological innovation with ethical responsibility and teacher involvement, to ensure that AI serves as a pedagogically sound and supportive tool in higher education.

Inclusive Eco-Innovation in Schools: Biomaterials, Service-Learning, and Community Environmental Awareness

Eduardo Ubeda, Colegio Areteia, Madrid, Spain

This session presents a transformative educational initiative from Colegio Areteia, a school specializing in students with learning differences, which integrates ecology, inclusion, and innovation through service-learning and STEAM-based activities.

At the heart of the initiative is the Eco-Store, a student-led project where learners create, market, and sell eco-friendly products—including handmade soaps, terrariums, and candles—to raise funds for an animal shelter. This project develops entrepreneurial, scientific, and social skills while reinforcing environmental values.

Complementing this, the School Bioengineering Project focuses on designing sustainable materials such as bioplastics, bioceramics, and recycled paper objects, using organic and household waste…

Keywords: Sustainability, Inclusion, Biomaterials, STEAM Education, Service-Learning

Inclusive Eco-Innovation in Schools: Biomaterials, Service-Learning, and Community Environmental Awareness

Eduardo Ubeda

This session presents a transformative educational initiative from Colegio Areteia, a school specializing in students with learning differences, which integrates ecology, inclusion, and innovation through service-learning and STEAM-based activities.

At the heart of the initiative is the Eco-Store, a student-led project where learners create, market, and sell eco-friendly products—including handmade soaps, terrariums, and candles—to raise funds for an animal shelter. This project develops entrepreneurial, scientific, and social skills while reinforcing environmental values.

Complementing this, the School Bioengineering Project focuses on designing sustainable materials such as bioplastics, bioceramics, and recycled paper objects, using organic and household waste. Students showcased their work through interactive workshops at the “Madrid es Ciencia” Fair, inspiring both participants and visitors.

Additional school-wide initiatives include classroom recycling systems, energy efficiency audits using Arduino sensors, biodiversity mapping, and partnerships with NGOs like WWF, Greenpeace, and Afanías. Events like EcoWeek and river cleanups provide hands-on experiences that promote environmental stewardship and community engagement.

The program’s success is marked by the renewal of the international Eco-Schools Green Flag, recognizing the school’s long-term ecological commitment. Designed to be replicable, these projects demonstrate that impactful environmental education is achievable with minimal resources and strong community involvement.

This session will share practical methodologies, outcomes, and lessons learned, offering an inclusive, scalable model for schools to integrate science, creativity, and sustainability education, while fostering students’ agency and real-world impact.

Enhancing Personalization and Efficiency of Learning with an In-Course GenAI Tutor

Alexandra Urban, Ph.D., Olwen Puralena and Sandra Delgado Betancourth, Coursera, Mountain View, California, USA

This study examines how targeted Generative Artificial Intelligence (GenAI) enhancements can elevate both business outcomes and the learner experience for large-scale online learning environments. Focusing on Coursera Coach, an in-course AI tutor designed to improve learner engagement and mastery, we evaluate two major platform innovations: long-term memory for personalized, context-aware support and response streaming for faster, more fluid interactions. Through a mixed-methods approach combining randomized controlled trials, platform-level behavioral metrics, and in-depth qualitative research, the findings reveal that these enhancements reshape how learners perceive and utilize AI tutoring.

Quantitatively, streaming reduced perceived latency by 71%, leading to substantial improvements in learner sentiment, including a 33% increase in daily likes and a 27% decrease in dislikes…

Keywords: Generative AI (GenAI), AI Tutor, Personalized Learning, Engagement, Online Learning

Enhancing Personalization and Efficiency of Learning with an In-Course GenAI Tutor

Alexandra Urban, Ph.D., Olwen Puralena and Sandra Delgado Betancourth

This study examines how targeted Generative Artificial Intelligence (GenAI) enhancements can elevate both business outcomes and the learner experience for large-scale online learning environments. Focusing on Coursera Coach, an in-course AI tutor designed to improve learner engagement and mastery, we evaluate two major platform innovations: long-term memory for personalized, context-aware support and response streaming for faster, more fluid interactions. Through a mixed-methods approach combining randomized controlled trials, platform-level behavioral metrics, and in-depth qualitative research, the findings reveal that these enhancements reshape how learners perceive and utilize AI tutoring.

Quantitatively, streaming reduced perceived latency by 71%, leading to substantial improvements in learner sentiment, including a 33% increase in daily likes and a 27% decrease in dislikes. Long-term memory scaled rapidly, with nearly 200,000 stored memories and 34,000 daily retrievals as of August 2025, enabling meaningful personalization at scale. Notably, the Coach Tutor experiment produced a statistically significant lift in next-day paid active retention, demonstrating direct business impact. Learners have interacted with Coursera Coach more than 32 million times in the first three quarters of 2025 alone.

Qualitatively, learners described Coursera Coach as shifting from an optional add-on to an essential learning partner that improves comprehension and retention of course materials, boosts confidence, and bridges gaps between tutor-led and self-directed study, giving learners a framework of how to learn. Together, these results highlight the importance of speed, personalization, and seamless integration in maximizing the value of GenAI-powered tutoring.

AI Literacy and Creative Coding as Predictors of Primary Students’ Classroom Engagement

Maribel Miranda-Pinto, Jose Manuel Saez-Lopez, Ph.D., and Esteban Vazquez-Cano, Ph.D., Universidad Nacional de Educación a Distancia, Madrid, Spain

The growing integration of block-based programming and educational robotics in primary education has intensified the need to understand how young learners develop meaningful and sustained engagement with digital learning activities. Yet empirical evidence remains limited regarding how conceptual knowledge of Artificial Intelligence (AI) interacts with hands-on coding practices to shape students’ participation and motivation. This study proposes a predictive framework that models classroom engagement as the joint outcome of two complementary dimensions: (a) AI-related conceptual understanding, including foundational ideas about algorithms and machine learning, and (b) practical–creative experiences with visual programming and robotics. Drawing on multivariate analyses, specifically PLS2 regression, response surface modeling, and moderated interaction testing, the findings reveal a dual latent structure. One dimension reflects experiential engagement rooted in creative coding, Scratch projects, and robotics-based problem solving, while the second captures students’ conceptual awareness of intelligent systems and computational processes…

Keywords: AI Literacy, Creative Coding, Educational Robotics, Student Engagement, Primary Education

AI Literacy and Creative Coding as Predictors of Primary Students’ Classroom Engagement

Maribel Miranda-Pinto, Jose Manuel Saez-Lopez, Ph.D., and Esteban Vazquez-Cano, Ph.D.

The growing integration of block-based programming and educational robotics in primary education has intensified the need to understand how young learners develop meaningful and sustained engagement with digital learning activities. Yet empirical evidence remains limited regarding how conceptual knowledge of Artificial Intelligence (AI) interacts with hands-on coding practices to shape students’ participation and motivation. This study proposes a predictive framework that models classroom engagement as the joint outcome of two complementary dimensions: (a) AI-related conceptual understanding, including foundational ideas about algorithms and machine learning, and (b) practical–creative experiences with visual programming and robotics. Drawing on multivariate analyses, specifically PLS2 regression, response surface modeling, and moderated interaction testing, the findings reveal a dual latent structure. One dimension reflects experiential engagement rooted in creative coding, Scratch projects, and robotics-based problem solving, while the second captures students’ conceptual awareness of intelligent systems and computational processes.

Results indicate that engagement increases most sharply when both dimensions are simultaneously high, demonstrating a clear synergistic rather than additive pattern. Students who combine conceptual insight with active exploration tend to approach coding tasks with greater agency, curiosity, and cognitive clarity. This integrated profile leads to stronger behavioral, affective, and cognitive engagement across disciplinary contexts. Cross-software replication using open-source tools confirms the robustness and reproducibility of the predictive model.

The study contributes a theoretically grounded and empirically validated explanation of how early AI literacy enhances the motivational value of practical coding experiences. It offers actionable implications for designing learning environments that fuse conceptual reflection with creative technological production, supported by examples of robotics activities, artistic programming tasks, and scaffolded problem-solving challenges. The findings provide a scalable framework for educators seeking to strengthen authentic engagement in primary computing education through coherent, theory-informed pedagogical integration.

Integrating Holistic Curriculum Design into Learning Communities: A Pathway to Whole-Person Education

Rachel Vincitore, Ed.D., Norfolk State University, Norfolk, Virginia, USA

Learning Communities aims to foster a supportive academic and social environment that aids students in their transition from high school to college and throughout their university experience. This foundation is established through classroom learning, co-curricular engagement, and meaningful interactions with peers, faculty, and staff. Incorporating a holistic curriculum model that intentionally addresses the intellectual, emotional, physical, and spiritual dimensions of learning can enhance these communities by creating more inclusive and transformative educational experiences. Because learning communities already emphasize cohort-based, theme-driven learning and strong faculty-student relationships, they are a natural setting for holistic approaches. Together, these models support students in exploring their identities, cultivating self-worth, and building resilience in a rapidly changing world. By co-creating holistic environments, educators can inspire deeper learning and personal growth, empowering students to connect with themselves, others, and the world around them.

Keywords: Holistic Curriculum, Learning Communities, Special Programs, Retention, Engagement

Integrating Holistic Curriculum Design into Learning Communities: A Pathway to Whole-Person Education

Rachel Vincitore, Ed.D.

Learning Communities aims to foster a supportive academic and social environment that aids students in their transition from high school to college and throughout their university experience. This foundation is established through classroom learning, co-curricular engagement, and meaningful interactions with peers, faculty, and staff. Incorporating a holistic curriculum model that intentionally addresses the intellectual, emotional, physical, and spiritual dimensions of learning can enhance these communities by creating more inclusive and transformative educational experiences. Because learning communities already emphasize cohort-based, theme-driven learning and strong faculty-student relationships, they are a natural setting for holistic approaches. Together, these models support students in exploring their identities, cultivating self-worth, and building resilience in a rapidly changing world. By co-creating holistic environments, educators can inspire deeper learning and personal growth, empowering students to connect with themselves, others, and the world around them.

AI-Powered Business Mentorship in Uganda: ChatGPT Adoption by Micro and Small Enterprises

Samuel Walulumba and Saadat Lubowa Kimuli Nakyejwe, Ph.D., Makerere University Business School, Kampala, Uganda

This study investigates the adoption and role of ChatGPT as an AI-powered business mentor among Micro and Small Enterprises (MSEs) in Uganda, a context where access to formal business training, mentorship and advisory services is limited. It explores how entrepreneurs leverage mobile-accesssible generative AI tools to support just-in-time learning, operational problem-solving and strategic decision making in resource-constrained settings. Data were collected through semi-structured interviews with 28 MSE owners across Kampala, Jinja, Mbale and Gulu. Findings reveal that ChatGPT is strategically used for price optimization, financial record-keeping, drafting customer service scripts, generating localized digital marketing content (e.g WhatsApp), and navigating regulatory compliance with agencies such as URSB and URA…

Keywords: Generative Artificial Intelligence, Micro and Small Enterprises, Digital Mentorship, Entrepreneurial Learning, Uganda

AI-Powered Business Mentorship in Uganda: ChatGPT Adoption by Micro and Small Enterprises

Samuel Walulumba and Saadat Lubowa Kimuli Nakyejwe, Ph.D.

This study investigates the adoption and role of ChatGPT as an AI-powered business mentor among Micro and Small Enterprises (MSEs) in Uganda, a context where access to formal business training, mentorship and advisory services is limited. It explores how entrepreneurs leverage mobile-accesssible generative AI tools to support just-in-time learning, operational problem-solving and strategic decision making in resource-constrained settings. Data were collected through semi-structured interviews with 28 MSE owners across Kampala, Jinja, Mbale and Gulu. Findings reveal that ChatGPT is strategically used for price optimization, financial record-keeping, drafting customer service scripts, generating localized digital marketing content (e.g WhatsApp), and navigating regulatory compliance with agencies such as URSB and URA. Participants highlighted the tool’s immediacy, multilingual capacity (English and Luganda), and contextual adaptability through tailored prompts. A key insight is the emergency of anticipatory learning, where entrepreneurs simulate hypothetical business scenarios, such as supplier price fluctuations or seasonal demand changes, to rehearse responses before implementation. This demonstrates that AI functions not merely as a reactive advisory tool but as a catalyst for agile, adaptive entrepreneurial capability. The study emphasizes the potential of AI-powered mentorship to democratize access to actionable business intelligence for Uganda’s estimated 2.8 million MSEs. It recommends integrating AI literacy into national business development services programs and promoting localized prompt engineering to enhance contextual relevance, responsible adoption and practical entrepreneurial impact.

Informality as Innovation: Rethinking Small Business Learning in Ugandan Markets through Adaptive, Peer-Led Models

Samuel Walulumba and Saadat Lubowa Kimuli Nakyejwe, Ph.D., Makerere University Business School, Kampala, Uganda

This study investigates how informal learning practices function as innovative knowledge systems for micro and small enterprises (MSEs) in Uganda. Using a qualitative research design, the study collected data from 32 entrepreneurs through in-depth interviews and four market-based focus groups conducted in Kampala, Mukono and Jinja. The objective was to uncover how learning unfolds in informal settings where entrepreneurs lack structured training access. Findings show that peer-led learning through observation, imitation, WhatsApp discussions and real-time problem-solving forms the backbone of capability development in informal markets. Entrepreneurs rely heavily on trusted peers for troubleshooting, pricing decisions, customer negotiation strategies and resource improvization. Informal networks also act as rapid feedback systems that allow micro-experimentation and collective risk-sharing…

Keywords: Informal Learning, African Entrepreneurship, Peer-Led Models, Adaptive Learning, Microenterprise Development

Informality as Innovation: Rethinking Small Business Learning in Ugandan Markets through Adaptive, Peer-Led Models

Samuel Walulumba and Saadat Lubowa Kimuli Nakyejwe, Ph.D.

This study investigates how informal learning practices function as innovative knowledge systems for micro and small enterprises (MSEs) in Uganda. Using a qualitative research design, the study collected data from 32 entrepreneurs through in-depth interviews and four market-based focus groups conducted in Kampala, Mukono and Jinja. The objective was to uncover how learning unfolds in informal settings where entrepreneurs lack structured training access. Findings show that peer-led learning through observation, imitation, WhatsApp discussions and real-time problem-solving forms the backbone of capability development in informal markets. Entrepreneurs rely heavily on trusted peers for troubleshooting, pricing decisions, customer negotiation strategies and resource improvization. Informal networks also act as rapid feedback systems that allow micro-experimentation and collective risk-sharing. The study concludes that informality is not merely a survival mechanism but a highly adaptive innovation-driven learning model. Training programs that ignore this reality often fail due to misalignment with local business culture. The study recommends integrating peer-led mentorship circles, market-based apprenticeships, and mobile micro-coaching into enterprise development programs. Policymakers and NGOs should design entrepreneurship support that strengthens existing informal learning ecosystems rather than replacing them with rigid formal models.

The Influence Effect: How to Lead through Change Without Burning Out

Alfred Washington, Designed to Lead, Dallas, Texas, USA

In today’s rapidly evolving learning and organizational environments, educators and leaders are expected to manage constant change while maintaining clarity, connection, and creativity. Yet the pressure to adapt often leads to burnout, disengagement, and emotional fatigue.

This session explores The Influence Effect — a practical framework designed to help educators, trainers, and learning professionals expand their impact without compromising their well-being. Drawing from leadership psychology, behavioral science, and real-world experience, Alfred “AJ” Washington introduces strategies for sustaining purpose, improving communication, and building resilience within teams and classrooms.

Participants will learn how to:
Reframe pressure as a tool for growth and innovation…

Keywords: Leadership, Resilience, Education, Communication, Innovation

The Influence Effect: How to Lead through Change Without Burning Out

Alfred Washington

In today’s rapidly evolving learning and organizational environments, educators and leaders are expected to manage constant change while maintaining clarity, connection, and creativity. Yet the pressure to adapt often leads to burnout, disengagement, and emotional fatigue.

This session explores The Influence Effect — a practical framework designed to help educators, trainers, and learning professionals expand their impact without compromising their well-being. Drawing from leadership psychology, behavioral science, and real-world experience, Alfred “AJ” Washington introduces strategies for sustaining purpose, improving communication, and building resilience within teams and classrooms.

Participants will learn how to:

Reframe pressure as a tool for growth and innovation.

Lead with emotional intelligence and empathy, even in uncertainty.

Strengthen influence and trust through authentic presence.

Apply actionable tools that turn reflection into results.

The session blends storytelling, evidence-based insights, and guided discussion, offering attendees both inspiration and immediate takeaways they can integrate into their work.

Transforming Flipped Classrooms with Generative AI: Insights from 70+ Students across Multiple Courses and Academic Levels

Suhail Y Tayeb and Hui Soo Chae, Ed.D., New York University, New York, New York, USA

This session presents a multi-course analysis of how AI tools and flipped classroom design reshape student learning, engagement, confidence, and preparedness in higher education. Drawing on a uniquely comprehensive dataset collected over multiple semesters across undergraduate and graduate real estate courses at New York University, the study includes more than 70 students representing diverse linguistic, cultural, and academic backgrounds. The findings offer rare insight into how students actually use Generative AI to deepen comprehension, prepare peer-led activities, summarize complex content, and accelerate the design of presentations and learning artifacts.

The data reveal clear patterns: students report higher engagement, stronger recall, improved confidence in public speaking, and greater agency over their learning when AI-supported flipped formats are used. Non-native English speakers particularly benefit from transcripts, subtitles, and AI-generated linguistic scaffolding, which reduce cognitive load and increase participation…

Keywords: Flipped Classroom, AI in Higher Education, Student Engagement, Learning Analytics, Instructional Design

Transforming Flipped Classrooms with Generative AI: Insights from 70+ Students across Multiple Courses and Academic Levels

Suhail Y Tayeb and Hui Soo Chae, Ed.D.

This session presents a multi-course analysis of how AI tools and flipped classroom design reshape student learning, engagement, confidence, and preparedness in higher education. Drawing on a uniquely comprehensive dataset collected over multiple semesters across undergraduate and graduate real estate courses at New York University, the study includes more than 70 students representing diverse linguistic, cultural, and academic backgrounds. The findings offer rare insight into how students actually use Generative AI to deepen comprehension, prepare peer-led activities, summarize complex content, and accelerate the design of presentations and learning artifacts.

The data reveal clear patterns: students report higher engagement, stronger recall, improved confidence in public speaking, and greater agency over their learning when AI-supported flipped formats are used. Non-native English speakers particularly benefit from transcripts, subtitles, and AI-generated linguistic scaffolding, which reduce cognitive load and increase participation. Students also provide candid feedback about pacing, activity formats, workload balance, and where instructor guidance remains essential.

This session will share aggregated results, thematic analyses, student quotes, and actionable design principles for educators seeking to incorporate AI into flipped learning environments. Attendees will leave with a research-backed framework for implementing scalable, inclusive, and pedagogically sound AI-enabled course models that support deeper learning and real-world skill development across disciplines.

AI-Driven Academic Advising and Career Preparation

Suhail Y Tayeb, New York University, New York, New York, USA

The Career Intelligence Platform is an AI-enabled advising and career-preparation system designed to offer scalable, personalized academic guidance, resume support, and interview practice for higher education students. Developed and piloted at NYU’s Schack Institute of Real Estate, the platform brings together three modules — PathwayIQ, CareerIQ, and CollegeIQ — within a unified, avatar-led experience that supports learners across the full academic and career journey.

PathwayIQ provides individualized academic advising using a department-specific knowledge base aligned to courses, concentrations, and career pathways. CareerIQ offers targeted resume feedback based on job descriptions, identifies skill gaps, and conducts realistic mock interviews tailored to the likely expectations of different roles and industries. CollegeIQ assists pre-college students by guiding them through college essays and application preparation. All modules are delivered through a multilingual avatar interface that can interact in more than 25 languages, offering an accessible and culturally adaptable student experience…

Keywords: AI Advising, Career Readiness, Resume Optimization, Interview Simulation, Higher Education Technology

AI-Driven Academic Advising and Career Preparation

Suhail Y Tayeb

The Career Intelligence Platform is an AI-enabled advising and career-preparation system designed to offer scalable, personalized academic guidance, resume support, and interview practice for higher education students. Developed and piloted at NYU’s Schack Institute of Real Estate, the platform brings together three modules — PathwayIQ, CareerIQ, and CollegeIQ — within a unified, avatar-led experience that supports learners across the full academic and career journey.

PathwayIQ provides individualized academic advising using a department-specific knowledge base aligned to courses, concentrations, and career pathways. CareerIQ offers targeted resume feedback based on job descriptions, identifies skill gaps, and conducts realistic mock interviews tailored to the likely expectations of different roles and industries. CollegeIQ assists pre-college students by guiding them through college essays and application preparation. All modules are delivered through a multilingual avatar interface that can interact in more than 25 languages, offering an accessible and culturally adaptable student experience.

In an early pilot, more than 60 students used the platform to receive personalized academic recommendations and refine their resumes. Students described the experience as clear, intuitive, and highly efficient, especially when navigating course–career alignment and preparing job materials. Faculty and staff noted its potential to ease advising bottlenecks and deliver consistent, high-quality guidance at scale.

This demonstration will showcase how students interact with the platform, how the system structures guidance, and how the interview simulation operates. Attendees will gain insight into the practical role AI-enabled advising tools can play in strengthening student readiness while reducing institutional workload.

The Evolution of Learning Statistics in Shaping Students’ Attitudes and Performance: A Comparative Analysis

Huay Woon You, Ph.D., Universiti Kebangsaan Malaysia, Selangor, Malaysia

It has been known that statistical knowledge is essential in academic or professional careers. Students’ attitudes toward statistics play a crucial role in their learning experience and overall success in the subject. This study aimed to analyze 90 pre-university students’ attitudes toward Statistics using pre-test and post-test surveys. The students are required to undertake the Statistics course in the first semester. The pre- and post-tests were conducted during the first week and final week (week 17) of the semester, respectively. The Survey of Attitude Towards Statistics (SATS) was used and it contains 36 items that assess six dimensions, namely effort, affective, cognitive competence, difficulty, value and interest. The average score for all the dimensions during post-test is lower compared to the pre-test. In addition, there is a significant difference between gender for the effort dimension in the pre- and post-tests…

Keywords: Statistics, Logistic Regression, Pre-University, Attitude, Performance

The Evolution of Learning Statistics in Shaping Students’ Attitudes and Performance: A Comparative Analysis

Huay Woon You, Ph.D.

It has been known that statistical knowledge is essential in academic or professional careers. Students’ attitudes toward statistics play a crucial role in their learning experience and overall success in the subject. This study aimed to analyze 90 pre-university students’ attitudes toward Statistics using pre-test and post-test surveys. The students are required to undertake the Statistics course in the first semester. The pre- and post-test were conducted during the first week and final week (week 17) of the semester, respectively. The Survey of Attitude Towards Statistics (SATS) was used and it contains 36 items that assess six dimensions, namely effort, affective, cognitive competence, difficulty, value and interest. The average score for all the dimensions during post-test is lower compared to the pre-test. In addition, there is a significant difference between gender for the effort dimension in the pre- and post-tests. On the contrary, the post-test results indicated a significant difference between genders related to cognitive competence, value and interest dimensions. In terms of the expected performance, there are 76 students expected to obtain As during the pre-test. However, the number of students who expected As decreased from 76 respondents to 51 respondents, with a decrease of 32.89%, during the post-test. Overall, there are 42 students who obtained As, which is lower than the expected A grades during the pre-test and post-test. The correlation investigation revealed a weak negative relationship between difficulty dimension with effort and value dimensions for the pre- and post-tests. Multinomial logistic regression was conducted and found that difficulty dimension is a positive predictor in predicting the performance of students on the Statistics subject. Implications from the results of this study might suggest that the collaborative effort from researchers and instructors to enhance the teaching and learning of statistics to reveal optimistic results.

Words that Nourish: Implementing the Level Up Pyramid (LUP) Framework through a Project on Nutrition and Language Education

Aikaterini Zafiri and Vasiliki Papathanasiou, Atsoglou School, Korinthos, Greece

This presentation explores the implementation of the Level Up Pyramid (LUP) framework through an interdisciplinary language project focused on nutrition and well-being. Designed for lower secondary students, the project integrates linguistic objectives—grammar, vocabulary, and expressive writing—with values education inspired by the four tiers of the LUP: effort, consistency, cooperation, and civic responsibility.

The theme of nutrition serves as both a linguistic and ethical field for exploration. Students engage in reading, writing, and creative activities around food culture, sustainability, and healthy habits while reflecting on the moral dimensions of choice, balance, and self-discipline. Through gamified elements such as “value cards” and reflective feedback charts, learners experience how language can shape not only communication but also character and community awareness…

Keywords: Level Up Pyramid (LUP), Project-Based Learning, Language Education, Nutrition, Values

Words that Nourish: Implementing the Level Up Pyramid (LUP) Framework through a Project on Nutrition and Language Education

Aikaterini Zafiri and Vasiliki Papathanasiou

This presentation explores the implementation of the Level Up Pyramid (LUP) framework through an interdisciplinary language project focused on nutrition and well-being. Designed for lower secondary students, the project integrates linguistic objectives—grammar, vocabulary, and expressive writing—with values education inspired by the four tiers of the LUP: effort, consistency, cooperation, and civic responsibility.

The theme of nutrition serves as both a linguistic and ethical field for exploration. Students engage in reading, writing, and creative activities around food culture, sustainability, and healthy habits while reflecting on the moral dimensions of choice, balance, and self-discipline. Through gamified elements such as “value cards” and reflective feedback charts, learners experience how language can shape not only communication but also character and community awareness.

The session will present classroom practices, student outputs, and teacher reflections demonstrating how the LUP transforms a language lesson into a holistic learning experience. By combining the vocabulary of food with the values of growth, the project illustrates how gamified, values-based pedagogy can nourish both linguistic and ethical development in contemporary education.

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