2025 Conference Program

Online-Only Days: Wednesday, May 28th | Thursday, May 29th

Hybrid Days: Wednesday, June 11th | Thursday, June 12th | Friday, June 13th

Wednesday, June 11th, 2025
(Hybrid, In New York and Online)

All times are Eastern time. All sessions will be streamed online and all virtual sessions will be shown in an area at the in-person venue. In addition, all sessions will be recorded for registered attendees.


The Presidential Rooms are on the 3rd floor of Faculty House, the event venue, the Seminar Rooms are on the 2nd floor, the Ivy Lounge is on the 1st floor.

12:00 PM - DOORS OPEN

1:00 PM - 2:15 PM - OPENING SESSION
PRESIDENTIAL ROOM 1

1:00 PM - 1:15 PM

Opening Session

David Guralnick, Ph.D.
President and CEO
Kaleidoscope Learning
New York, New York, USA

1:15 PM - 2:15 PM

Keynote Speech
Digital Learning Games: From Child to Adult Learning

Bruce Martin McLaren, Ph.D.
Full Professor, Carnegie Mellon University
Pittsburgh, Pennsylvania, USA

Digital learning games have become a hot topic in educational technology and Learning Science research. My own lab, the McLearn Lab, has developed several digital learning games for late elementary and middle school students. One of these games, Decimal Point, has been utilized by over 1,500 middle school students in classroom studies over the past decade. In these studies, we explored a variety of game-based learning and Learning Science principles. In this talk, I will provide an overview of digital game-based learning (DGBL) and briefly discuss the games developed by my lab. More importantly, I will share insights into the impact of DGBL on adult learning. Finally, I will discuss my thoughts on the future of DGBL, particularly in adult education. 

Speaker bio

2:15 PM - 2:45 PM - BREAK

2:45 PM - 4:15 PM - PARALLEL SESSIONS

TRACK 1 [IN-PERSON] - SESSION 1G
PRESIDENTIAL ROOM 1
Session Chair: TBD
2:45 PM - 4:15 PM

2:45 PM - 3:45 PM

Think Small to Win Big: Unlocking Learning Innovation with Agile Experimentation

Suzy Robertson, McKinsey & Company, Lagunilla, Heredia, Costa Rica, and Gia Fanelli, McKinsey & Company, Stamford, Connecticut, USA

Change is constant, and the pressure to keep up can feel overwhelming, but big breakthroughs don’t always require big moves, especially when working within the realities of an organization where resources, time, and capacity can limit traditional research approaches. Small, focused experiments offer a powerful way forward.

In McKinsey’s R&I Learning Lab, we’ve seen how these adaptable methods can drive meaningful results—whether you’re designing learning experiences, advancing education strategies, or building innovative solutions. We’ll share an agile framework to help you test ideas quickly, learn fast, and make progress within your organization’s constraints. Together, we’ll explore how to shape a strong hypothesis, set up experiments that deliver real insights, and turn those insights into strategies that stick. We’ll discuss practical strategies for gaining leadership buy-in to help you secure the support needed to scale your efforts. To illustrate the framework in action, we’ll share stories from our own experimentation efforts…

Keywords: experimentation, innovation, agile, problem-solving

Think Small to Win Big: Unlocking Learning Innovation with Agile Experimentation

Suzy Robertson and Gia Fanelli

Change is constant, and the pressure to keep up can feel overwhelming, but big breakthroughs don’t always require big moves, especially when working within the realities of an organization where resources, time, and capacity can limit traditional research approaches. Small, focused experiments offer a powerful way forward.

In McKinsey’s R&I Learning Lab, we’ve seen how these adaptable methods can drive meaningful results—whether you’re designing learning experiences, advancing education strategies, or building innovative solutions. We’ll share an agile framework to help you test ideas quickly, learn fast, and make progress within your organization’s constraints. Together, we’ll explore how to shape a strong hypothesis, set up experiments that deliver real insights, and turn those insights into strategies that stick. We’ll discuss practical strategies for gaining leadership buy-in to help you secure the support needed to scale your efforts. To illustrate the framework in action, we’ll share stories from our own experimentation efforts.

What you’ll learn:
1. How to get leadership support for experimentation and innovation
2. How to design actionable experiments that drive meaningful progress
3. Practical methods to measure outcomes and make informed decisions

3:45 PM - 4:15 PM

How AI-Powered Grading Boosts Completion through Faster, High-Quality Feedback at Scale

Alexandra Urban, Ed.D., Robert Urbaniak, Coursera, Mountain View, California, USA, and Xiaonan Sun, Ph.D, Coursera, Toronto, Ontario, Canada

Peer review assessments in large-scale online courses face challenges around inconsistency, delays, varying feedback quality, and concerns over the expertise of student graders. While peer assessment can be implemented at scale, providing efficient and effective feedback remains difficult. To address these issues, a leading online education platform implemented an AI Grading system leveraging generative artificial intelligence (GenAI) to provide immediate, consistent, and scalable feedback aligned with instructor rubrics.

An initial beta test graded approximately 300,000 text submissions across several courses. Key metrics showed the AI system provided feedback 45 times faster than human grading, with 90% of learners satisfied with the AI feedback. Interestingly, first attempt pass rates were lower (72% vs 88%) and average grades were 3% lower than human grading, suggesting increased rigor. At the same time, course completions rose 16.7% with faster AI grading, a promising sign of increased learner engagement and persistence…

Keywords: peer review, completion, feedback, generative AI, learning analytics

How AI-Powered Grading Boosts Completion through Faster, High-Quality Feedback at Scale

Alexandra Urban, Ed.D., Robert Urbaniak, and Xiaonan Sun, Ph.D.

Peer review assessments in large-scale online courses face challenges around inconsistency, delays, varying feedback quality, and concerns over the expertise of student graders. While peer assessment can be implemented at scale, providing efficient and effective feedback remains difficult. To address these issues, a leading online education platform implemented an AI Grading system leveraging generative artificial intelligence (GenAI) to provide immediate, consistent, and scalable feedback aligned with instructor rubrics.

An initial beta test graded approximately 300,000 text submissions across several courses. Key metrics showed the AI system provided feedback 45 times faster than human grading, with 90% of learners satisfied with the AI feedback. Interestingly, first attempt pass rates were lower (72% vs 88%) and average grades were 3% lower than human grading, suggesting increased rigor. At the same time, course completions rose 16.7% with faster AI grading, a promising sign of increased learner engagement and persistence.

While showing promise for scalable assessment, open questions remain around balancing rigor and accessibility, supporting learner adaptation, ensuring feedback relevance across subjects, determining which assignments still benefit from human collaboration, and addressing ethical considerations around transparency, fairness, and human oversight. The results demonstrate AI’s potential to enhance assessment scalability and speed while providing rich data to inform improvements. Careful balancing of AI and human expertise will continue to be paramount as further AI integrations are developed for online courses.

TRACK 2 [IN-PERSON] - SESSION 2G
PRESIDENTIAL ROOM 2
Session Chair: TBD
2:45 PM - 4:15 PM

2:45 PM - 3:45 PM

Bridging the Digital Divide: AI-Powered Learning in Low-Resource Environments

Kevin Martin, Ph.D., Digital Education Futures Initiative (DEFI), The Bridge, Hughes Hall, University of Cambridge, Cambridge, United Kingdom

In regions with limited infrastructure, low connectivity, and scarce resources, digital learning has often failed to meet the needs of learners. Drawing on the transformative work of the TIST Learning Centre in rural East Africa, this talk explores how artificial intelligence can be harnessed to foster inclusive education in low-resource environments. By integrating dialogic pedagogy, indigenous learning traditions, and AI tools such as offline GPTs and WhatsApp chatbots, we present scalable solutions that align with local needs.

The presentation will examine practical design strategies, including offline content sharing, lightweight app development, and peer-facilitated learning models, all of which prioritize accessibility and community engagement. Case studies of subsistence farmers using these tools for agricultural education, community leadership, and environmental sustainability will illustrate how AI might empower learners and reduce the digital divide.

We will also discuss challenges such as power constraints, cost barriers, and regional adaptation, alongside the potential for extending these innovations to disaster response, teacher training, and underserved communities in high-resource settings. This talk offers a blueprint for using AI to create equitable, human-centered education systems, emphasizing collaboration and shared values.

Keywords: AI, low resource, digital divide, learning, pedagogy

Bridging the Digital Divide: AI-Powered Learning in Low-Resource Environments

Kevin Martin, Ph.D.

In regions with limited infrastructure, low connectivity, and scarce resources, digital learning has often failed to meet the needs of learners. Drawing on the transformative work of the TIST Learning Centre in rural East Africa, this talk explores how artificial intelligence can be harnessed to foster inclusive education in low-resource environments. By integrating dialogic pedagogy, indigenous learning traditions, and AI tools such as offline GPTs and WhatsApp chatbots, we present scalable solutions that align with local needs.

The presentation will examine practical design strategies, including offline content sharing, lightweight app development, and peer-facilitated learning models, all of which prioritize accessibility and community engagement. Case studies of subsistence farmers using these tools for agricultural education, community leadership, and environmental sustainability will illustrate how AI might empower learners and reduce the digital divide.

We will also discuss challenges such as power constraints, cost barriers, and regional adaptation, alongside the potential for extending these innovations to disaster response, teacher training, and underserved communities in high-resource settings. This talk offers a blueprint for using AI to create equitable, human-centered education systems, emphasizing collaboration and shared values.

3:45 PM - 4:15 PM

Streaming and Broadcasting Educational Aid Documents Where There Is No Internet or WiFi Service Available

David Glickman, P.E., and Professor Saul Troen, Ph.D., ChipServer LLC, New York, New York, USA

Imagine providing the ability for anyone to learn anywhere, at any time—whether in a classroom, on vacation, at home, or even gathered under a tree. Most document-sharing programs rely on an internet connection or WiFi to distribute files. But what if there’s no WiFi, the internet is unreliable, or it’s simply unsafe?

ChipServer is a revolutionary, patented device that changes the game. It allows users to securely access, share, and project documents without needing an internet connection, WiFi, downloadable apps, or even a computer. ChipServer creates its own private WiFi network, at no extra cost or subscription fees, to broadcast content to your students in a classroom, your audience in a lecture hall, or even your attendees on a field trip or in a park. It’s the freedom to learn anywhere—without limitations.

Keywords: patented streaming broadcasting device

Streaming and Broadcasting Educational Aid Documents Where There Is No Internet or WiFi Service Available

David Glickman, P.E., and Professor Saul Troen, Ph.D.

Imagine providing the ability for anyone to learn anywhere, at any time—whether in a classroom, on vacation, at home, or even gathered under a tree. Most document-sharing programs rely on an internet connection or WiFi to distribute files. But what if there’s no WiFi, the internet is unreliable, or it’s simply unsafe?

ChipServer is a revolutionary, patented device that changes the game. It allows users to securely access, share, and project documents without needing an internet connection, WiFi, downloadable apps, or even a computer. ChipServer creates its own private WiFi network, at no extra cost or subscription fees, to broadcast content to your students in a classroom, your audience in a lecture hall, or even your attendees on a field trip or in a park. It’s the freedom to learn anywhere—without limitations.

TRACK 3 [IN-PERSON] - SESSION 3G
PRESIDENTIAL ROOM 3
Session Chair: TBD
2:45 PM - 4:15 PM

2:45 PM - 3:45 PM

Death of the Discussion Board: Navigating Higher Education eLearning in the Age of Generative AI

Kristie Rankin, Ed.D., American Cast Iron Pipe, Birmingham, Alabama, USA

The landscape of higher education eLearning is undergoing rapid transformation due to the rise of generative AI technologies. The traditional discussion board assignment, once a cornerstone of online learning and critical engagement, is facing obsolescence as the risk of AI-assisted academic dishonesty grows. The ease of producing AI-generated content challenges the authenticity of student contributions, undermining trust in the educational process.

This session explores the implications of generative AI on academic integrity and proposes innovative, AI-integrated alternative assignments that uphold rigorous educational standards while embracing technological advancements ethically. Participants will gain practical insights, strategies, and hands-on examples to design authentic, future-proof assessments that foster critical thinking, creativity, and genuine student engagement in an AI-driven landscape. By integrating AI as a collaborative tool rather than an unchecked shortcut, educators can cultivate a learning environment that supports ethical tech use and prepares students for a technology-infused world. Attendees will leave equipped to inspire deeper, AI-informed learning that remains academically sound and relevant.

Keywords: AI, higher education, integrity, e-learning

Death of the Discussion Board: Navigating Higher Education eLearning in the Age of Generative AI

Kristie Rankin, Ed.D.

The landscape of higher education eLearning is undergoing rapid transformation due to the rise of generative AI technologies. The traditional discussion board assignment, once a cornerstone of online learning and critical engagement, is facing obsolescence as the risk of AI-assisted academic dishonesty grows. The ease of producing AI-generated content challenges the authenticity of student contributions, undermining trust in the educational process.

This session explores the implications of generative AI on academic integrity and proposes innovative, AI-integrated alternative assignments that uphold rigorous educational standards while embracing technological advancements ethically. Participants will gain practical insights, strategies, and hands-on examples to design authentic, future-proof assessments that foster critical thinking, creativity, and genuine student engagement in an AI-driven landscape. By integrating AI as a collaborative tool rather than an unchecked shortcut, educators can cultivate a learning environment that supports ethical tech use and prepares students for a technology-infused world. Attendees will leave equipped to inspire deeper, AI-informed learning that remains academically sound and relevant.

3:45 PM - 4:15 PM

A 3-S (Strength, Stamina, Speed) Model for Medical Education to Effectively Bridge Teaching and Learning

Mangala Sadasivan, Ph.D., Michigan State University, East Lansing, Michigan, USA, Bryan Kelly, D.O., Michigan State University College of Osteopathic Medicine, East Lansing, Michigan, USA, and Mary Hughes, D.O., Michigan State University, E. Lansing, Michigan, Michigan

The authors were interested in showing that a 3-S Model using a bridged curriculum design helps connect teaching and learning and improves students’ retention of basic science and clinical knowledge. The authors designed three learning modules using the 3-S Model within a systems course. 304 registered MSU osteopathic medical students (3 campuses) participated in this within-subjects designed study. Students were video coached on how to complete assignments. The instructor who designed the modules also used video lectures to help students master clinical concepts and link them to the bridge. Board style practice questions relevant to the modules were used to help students improve access (increasing speed) to stored content.

This data was then compared to students’ performance on a final comprehensive exam and their COMLEX medical board examinations. The authors used mean comparisons to evaluate students’ performances on module items (using 3-S Model) to non-module items on unit exams, final course exam and COMLEX medical board examination. The data shows that on average, students performed significantly better on module items compared to non-module items on exams 1 and 2…

Keywords: teaching, learning, processing, efficiency, model

A 3-S (Strength, Stamina, Speed) Model for Medical Education to Effectively Bridge Teaching and Learning

Mangala Sadasivan, Ph.D., Bryan Kelly, D.O., and Mary Hughes, D.O.

The authors were interested in showing that a 3-S Model using a bridged curriculum design helps connect teaching and learning and improves students’ retention of basic science and clinical knowledge. The authors designed three learning modules using the 3-S Model within a systems course. 304 registered MSU osteopathic medical students (3 campuses) participated in this within-subjects designed study. Students were video coached on how to complete assignments. The instructor who designed the modules also used video lectures to help students master clinical concepts and link them to the bridge. Board style practice questions relevant to the modules were used to help students improve access (increasing speed) to stored content.

This data was then compared to students’ performance on a final comprehensive exam and their COMLEX medical board examinations. The authors used mean comparisons to evaluate students’ performances on module items (using 3-S Model) to non-module items on unit exams, final course exam and COMLEX medical board examination. The data shows that on average, students performed significantly better on module items compared to non-module items on exams 1 and 2. Based on Quintile designation, the mean scores were higher for module items then non-module items and the difference in scores between items for Quintiles 1 and 2 were significantly better on exam 1 and the gap widens for all Quintile groups on exam 2 and disappears in exam 3. Based on COMLEX performance, all students on average as a group, whether they Passed or Failed, performed better on Module items then non-module items in all three exams. The gap between scores of module items for students who passed COMLEX to those who failed was greater on Exam 1 (14.3) than on Exam 2 (7.5) and Exam 3 (10.2). Data shows 3-S Model using a bridge effectively connects teaching and learning.

4:15 PM - 4:30 PM - MINI-BREAK

4:30 PM - 6:00 PM - PARALLEL SESSIONS

TRACK 1 [IN-PERSON] - SESSION 1H
PRESIDENTIAL ROOM 1
Session Chair: TBD
4:30 PM - 6:00 PM

4:30 PM - 5:30 PM

Transforming Teaching and Learning Experiences Through AI: AI as a Powerful Mindtool

Nada Dabbagh, Ph.D., George Mason University, Fairfax, Virginia, USA

As educators and academics, we are all struggling to figure out how to use AI in teaching and learning contexts. We need principles and guidelines that inform the integration of AI into the learning environment. With AI-powered chatbots, the time is ripe for applying tried and true theoretical frameworks such as generative learning, to the use of AI in teaching and learning contexts. Participants in this presentation will learn how to integrate AI as a MindTool for meaningful learning; i.e., learning that is active, constructive, intentional cooperative, and constructive (Dabbagh, Marra, and Howland, 2019). Mindtools are knowledge construction or knowledge representation tools that enable learners to think about what they know in meaningful ways. Mindtools scaffold different forms of reasoning about content and enable generative learning (e.g., explaining, visualizing, enacting). While there are several classes of Mindtools, such as semantic organization tools, dynamic modeling tools, content creation tools, collaboration and communication tools, information search and resource management tools, AI can be an exemplary mindtool…

Keywords: generative learning, generative AI, mindtools, meaningful learning

Transforming Teaching and Learning Experiences Through AI: AI as a Powerful Mindtool

Nada Dabbagh, Ph.D.

As educators and academics, we are all struggling to figure out how to use AI in teaching and learning contexts. We need principles and guidelines that inform the integration of AI into the learning environment. With AI-powered chatbots, the time is ripe for applying tried and true theoretical frameworks such as generative learning, to the use of AI in teaching and learning contexts. Participants in this presentation will learn how to integrate AI as a MindTool for meaningful learning; i.e., learning that is active, constructive, intentional cooperative, and constructive (Dabbagh, Marra, and Howland, 2019). Mindtools are knowledge construction or knowledge representation tools that enable learners to think about what they know in meaningful ways. Mindtools scaffold different forms of reasoning about content and enable generative learning (e.g., explaining, visualizing, enacting). While there are several classes of Mindtools, such as semantic organization tools, dynamic modeling tools, content creation tools, collaboration and communication tools, information search and resource management tools, AI can be an exemplary mindtool.

Generative AI can enable learners to engage in generative learning. Just like AI-powered chatbots generate text using LLMs, learners can generate meaningful knowledge and understanding using AI as a personal information interpretation tool, a cognitive assistant, and a learning partner and coach. Mindtools have pedagogical affordances that make them attractive for both learners and instructors. Mindtools allow for knowledge representation, organization and synthesis. Mindtools work with any discipline and are ubiquitous. When technology is used as a mindtool, the learner creates content and represents it via the affordances of the tool thus making external the knowledge structures so that they can be made available for reflection and feedback. Using Generative AI as a mindtool supports generative learning by creating a more interactive, personalized, and responsive educational environment. By leveraging generative AI's capabilities, educators can enhance the learning experience, making it more engaging and effective in helping students construct their own knowledge.

5:30 PM - 6:00 PM

How Many Steps Does It Take to Build a Learning Tool? An Overview of Our Learning Design Lab

Felix Brito, Ph.D., and Sara Ombres, Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA

At our institution, the Instructional Design team is committed to using research-based best practices to create high-quality courses. We focus on developing content that’s both accessible and user-friendly, and we continuously evaluate external tools to ensure they enhance the learning experience. However, we sometimes find that the tools we need either don’t exist or don’t fully meet our needs. With tight deadlines and heavy workloads, addressing these gaps can be a challenge.

To solve this, we launched the Learning Design Lab, a space where “what if” ideas are transformed into practical solutions. The Lab brings together cross-disciplinary teams to develop innovative tools and strategies outside of our regular course development process, allowing us to tackle unique challenges without disrupting our course schedules…

Keywords: learning design lab, instructional design, online learning, innovation

How Many Steps Does It Take to Build a Learning Tool? An Overview of Our Learning Design Lab

Felix Brito, Ph.D., and Sara Ombres

At our institution, the Instructional Design team is committed to using research-based best practices to create high-quality courses. We focus on developing content that’s both accessible and user-friendly, and we continuously evaluate external tools to ensure they enhance the learning experience. However, we sometimes find that the tools we need either don’t exist or don’t fully meet our needs. With tight deadlines and heavy workloads, addressing these gaps can be a challenge.

To solve this, we launched the Learning Design Lab, a space where “what if” ideas are transformed into practical solutions. The Lab brings together cross-disciplinary teams to develop innovative tools and strategies outside of our regular course development process, allowing us to tackle unique challenges without disrupting our course schedules.

Some recent successes include creating an interactive polling tool integrated into our learning management system, a GenAI tool for generating random discussion prompts, and personalized learning presentations for students. The Lab encourages creative problem-solving, focusing on “small wins” that can have a big impact.

This session is aimed at instructional designers and technologists. We’ll share how we set up the Learning Design Lab, showcase successful projects, and explain the steps we took to find solutions. Attendees will also participate in brainstorming activities to come up with “outside the box” ideas for their own institutions, using tools like Padlet to share insights and foster collaboration. By the end of the session, participants will be equipped with actionable steps to create innovation labs at their own institutions and develop small-scale tools for online learning.

TRACK 2 [IN-PERSON] - SESSION 2H
PRESIDENTIAL ROOM 2
Session Chair: TBD
4:30 PM - 6:00 PM

4:30 PM - 5:30 PM

Reimagining Education with Custom Bots: A Student-Centered Approach

Casandra Silva Sibilin, York College, CUNY, New York, New York, USA

This session explores the integration of AI in education through the lens of student perspectives and experiences in several philosophy of education sections spanning from Fall 2023 to Fall 2024. The exploration was comprised of two different projects. The first project engaged students in evaluating ChatGPT in various educational roles, including tutor, teacher’s assistant, motivational coach, philosopher, and educational reformer. Through hands-on experimentation, students critically examined ChatGPT's capabilities, uncovering its strengths, limitations, and the complexities of its use in academic contexts.

In the second project, students took on the role of creators, designing and testing custom AI bots for specific educational purposes. Across both projects, students contributed over 500 reflective posts, sharing their hopes, concerns, and insights about using AI in education. This session will demonstrate selected uses of ChatGPT and custom AI bots and will offer takeaways based on students’ reflections. Participants will learn practical strategies for encouraging students to think critically about AI tools.

Keywords: AI in education, philosophy of education, ChatGPT roles, custom bots, critical thinking

Reimagining Education with Custom Bots: A Student-Centered Approach

Casandra Silva Sibilin

This session explores the integration of AI in education through the lens of student perspectives and experiences in several philosophy of education sections spanning from Fall 2023 to Fall 2024. The exploration was comprised of two different projects. The first project engaged students in evaluating ChatGPT in various educational roles, including tutor, teacher’s assistant, motivational coach, philosopher, and educational reformer. Through hands-on experimentation, students critically examined ChatGPT's capabilities, uncovering its strengths, limitations, and the complexities of its use in academic contexts.

In the second project, students took on the role of creators, designing and testing custom AI bots for specific educational purposes. Across both projects, students contributed over 500 reflective posts, sharing their hopes, concerns, and insights about using AI in education. This session will demonstrate selected uses of ChatGPT and custom AI bots and will offer takeaways based on students’ reflections. Participants will learn practical strategies for encouraging students to think critically about AI tools.

5:30 PM - 6:00 PM

Human-Centered AI Solutions for Scalable eLearning Localization

Michael Anderson, Welocalize, Inc., New York, New York, USA

As organizations expand globally, the demand for localized eLearning and video content continues to grow. This session focuses on how AI is transforming localization processes, enabling organizations to scale efficiently while maintaining cultural relevance and content quality. By combining the speed and scalability of AI with the precision and creativity of human expertise, businesses can create engaging, multilingual content tailored to diverse audiences.

The discussion will cover practical applications of AI-powered tools, including automation in course creation, translation, and video localization workflows. Topics will also include how to balance AI-driven automation with human oversight to ensure nuanced and culturally appropriate results. Additionally, the session will highlight the benefits of integrated localization partnerships that bring creative and technical services together under one roof, simplifying global content delivery…

Keywords: e-learning, AI, localization, captioning, voiceovers

Human-Centered AI Solutions for Scalable eLearning Localization

Michael Anderson

As organizations expand globally, the demand for localized eLearning and video content continues to grow. This session focuses on how AI is transforming localization processes, enabling organizations to scale efficiently while maintaining cultural relevance and content quality. By combining the speed and scalability of AI with the precision and creativity of human expertise, businesses can create engaging, multilingual content tailored to diverse audiences.

The discussion will cover practical applications of AI-powered tools, including automation in course creation, translation, and video localization workflows. Topics will also include how to balance AI-driven automation with human oversight to ensure nuanced and culturally appropriate results. Additionally, the session will highlight the benefits of integrated localization partnerships that bring creative and technical services together under one roof, simplifying global content delivery.

Key Takeaways:
1. Enhancing eLearning Localization with AI: Learn how AI-powered tools streamline course localization, from subtitles and synthetic voiceovers to cultural adaptations such as name localization and currency adjustments. Understand the situations where AI-generated solutions excel and where human involvement remains essential.
2. Creating Localization-Ready Workflows: Identify tools and best practices that make it easier to localize content for global markets. Explore how AI and human collaboration optimize efficiency and maintain quality during post-editing and content review.
3. Innovations in Video Localization: Understand how AI improves video subtitling, dubbing, and on-screen text localization. Gain insights into how automated workflows save time and scale for large-volume video projects.
4. Future Trends in AI-Driven Localization: Explore upcoming advancements in AI technologies and how they will shape the future of eLearning and video localization.

This session will provide actionable insights for educators, content creators, and localization professionals. Attendees will leave with practical strategies to use AI tools effectively while leveraging human expertise for quality assurance, cultural sensitivity, and audience engagement.

TRACK 3 [IN-PERSON] - SESSION 3H
PRESIDENTIAL ROOM 3
Session Chair: TBD
4:30 PM - 6:00 PM

4:30 PM - 5:30 PM

Risk Taking in Questioning Assumptions and Exploring New Behaviors: The Role of Identity in Transformative Learning

Rajashi Ghosh, Ph.D., Pierre Faller, Ed.D., M.B.A., and Victoria J. Marsick, Ph.D, Teachers College, Columbia University, New York, New York, USA

Mezirow (1975) defines transformative learning (TL) as a rational process of perspective transformation which includes “interrogating our assumptions and then confronting the distortions in our meaning schemes and perspectives” (Lawrence, 2012, p. 472). This self-examination is precipitated by a disorienting experience which forces one to confront the realization that their current assumptions are not serving them well and hence, they feel disoriented with what they are experiencing. While the experience of disorientation can be an effective catalyst to bring on this realization, whether one gets to interrogate their assumptions is predicated on their ability to explore new behaviors that might contradict their current assumptions…

Keywords: identity intersections, risks, assumptions, new behaviors, transformative learning

Risk Taking in Questioning Assumptions and exploring New Behaviors: The Role of Identity in Transformative Learning

Rajashi Ghosh, Ph.D. Pierre Faller, Ed.D., and Victoria Marsick, Ph.D.

Mezirow (1975) defines transformative learning (TL) as a rational process of perspective transformation which includes “interrogating our assumptions and then confronting the distortions in our meaning schemes and perspectives” (Lawrence, 2012, p. 472). This self-examination is precipitated by a disorienting experience which forces one to confront the realization that their current assumptions are not serving them well and hence, they feel disoriented with what they are experiencing. While the experience of disorientation can be an effective catalyst to bring on this realization, whether one gets to interrogate their assumptions is predicated on their ability to explore new behaviors that might contradict their current assumptions.

These AI agents are designed to communicate in the target language, offering students a unique opportunity to practice and improve their language skills in real-time conversations. They are programmed to understand and produce natural language, adapting to different proficiency levels and learning styles. By interacting with these agents, students engage in a rich, immersive linguistic environment that challenges them to use the foreign language in a variety of contexts and social interactions.

However, the TL theory has not paid sufficient attention to what risks one’s identity might pose to their efforts of questioning assumptions and exploring new behaviors if their identities are minoritized and hence, lack power within their context. While Mezirow’s TL theory incorporates context in terms of acknowledging that one’s assumptions are derived from one’s early life context through cultural and social experiences (Schnepfleitner & Ferreira, 2021), his theory does not give as much attention to how challenging those assumptions within one’s current life contexts might pose risks if their identities are largely powerless within their contexts. Due to this gap, Mezirow’s TL theory has been critiqued to have conceptualized transformative learning as largely psychological and cognitive (Taylor & Cranton, 2013).

This session is designed to support attendees to reflect upon the risks they face in questioning their assumptions and experimenting with new behaviors needed to transform their perspectives shaping their disorienting experience. In that inquiry, we would invite them to explore how their identity intersections (e.g., gender, race, and sexual orientation) might be shaping the risks facing them in their efforts to transform their perspectives.

5:30 PM - 6:00 PM

Designing Diverse and Balanced Student Teams for Enhanced Collaborative Learning: A Hybrid Approach Using Genetic Algorithms and Student Social Network Analysis

Sherif Abdelhamid, Ph.D., and Mona Aly, Virginia Military Institute, Lexington, Virginia, USA

Team-based and collaborative learning have gained significant attention in educational research as they foster more profound understanding, critical thinking, and student engagement. These methods encourage active participation, facilitate the exchange of diverse skills and perspectives, and prepare students for real-world teamwork experiences which are essential in professional settings. However, creating effective and diverse student teams is a critical challenge in face-to-face and online educational settings, particularly in large-scale environments such as Massive Open Online Courses (MOOCs) where hundreds or thousands of students can be enrolled.

While several research works highlight the benefits of team-based and collaborative learning, research and technological gaps still exist in optimizing team formation and the availability and effectiveness of tools that can facilitate this process. To address these gaps, we present a novel web-based system and algorithm that facilitates the formation of heterogeneous student groups based on their skill diversity, social ties, and balanced group sizes...

Keywords: team-based learning, collaborative learning, heterogeneous student groups, genetic algorithms, social network analysis

Designing Diverse and Balanced Student Teams for Enhanced Collaborative Learning: A Hybrid Approach Using Genetic Algorithms and Student Social Network Analysis

Sherif Abdelhamid, Ph.D., and Mona Aly

Team-based and collaborative learning have gained significant attention in educational research as they foster more profound understanding, critical thinking, and student engagement. These methods encourage active participation, facilitate the exchange of diverse skills and perspectives, and prepare students for real-world teamwork experiences which are essential in professional settings. However, creating effective and diverse student teams is a critical challenge in face-to-face and online educational settings, particularly in large-scale environments such as Massive Open Online Courses (MOOCs) where hundreds or thousands of students can be enrolled.

While several research works highlight the benefits of team-based and collaborative learning, research and technological gaps still exist in optimizing team formation and the availability and effectiveness of tools that can facilitate this process. To address these gaps, we present a novel web-based system and algorithm that facilitates the formation of heterogeneous student groups based on their skill diversity, social ties, and balanced group sizes. The system enables teachers to define activities or projects, specify required skills, collect students' self-assessments, and visualize the student social network through a simple, easy-to-use interface. In return, students provide self-ratings on the required skills and select preferred team members. Behind the scenes, the system uses a novel hybrid algorithm integrating genetic algorithms and student social network analysis to generate diverse and balanced teams while respecting students' social ties and preferences. The algorithm optimizes three critical objectives: maximizing skill diversity, enhancing friendship closeness and social ties within groups, and achieving balanced group sizes.

Experimental results on varying-sized synthetic student networks demonstrate the system's reliability, robustness, and stability. The algorithm consistently performs well in creating diverse groups while maintaining social ties and near-perfect size balance across clusters. The results show that the system is exceptionally well-suited for large-scale classrooms and MOOCs, enabling personalized, inclusive, and effective team creation to foster collaborative learning in diverse educational contexts.

6:00 PM - DRINKS - IVY LOUNGE - 1st FLOOR