2026 AI-ALOE Mini Retreat 🔗

September 14, 2026

The 2026 AI-ALOE Mini Retreat was held virtually on September 11, with about 50 participants, including research fellows, teaching fellows, and the external advisory board. Discussions centered on evaluation and impact, with a particular focus on learning proficiency.

Our SMART team (Dr. Min Kyu Kim, Jinho Kim, Yoojin Bae) participated as one of the seven technology teams of AI-ALOE. During the first half of the session, Jinho Kim shared the approaches our team has used to measure impact, experimental design, and learning analytics. In the second half, Dr. Min Kyu Kim and Yoojin Bae focused on learning proficiency - how our team understands it and how we could measure it going forward through experimental design and learning analytics.

Read more about it here: https://lnkd.in/p/e8NW_QMf 

New Manuscript in Assessing Writing 🔗

September 12, 2026

We have a new manuscript published in Asessing Writing by Dr. Min Kyu Kim, Hyunkyu Han, Seora Kim, and Dr. Mohamed Shameer Abdeen.

Kim, M. K., Han, H., Kim, S., & Abdeen, M. S. (2026). AI-scaffolded summary writing for pre-class learning in an undergraduate physics course. Assessing Writing, 70, 101093. https://doi.org/10.1016/j.asw.2026.101093

Abstract:

Summary writing is a common write-to-learn strategy in undergraduate STEM education, particularly for pre-class learning. Yet producing summaries that demonstrate deep comprehension is demanding and often requires instructional support. In response, Automated Summary Evaluation (ASE) tools have been developed to provide formative assessment and feedback on student summaries. This study examines the effectiveness of a generative AI-powered ASE tool designed to scaffold student engagement in pre-class summarization. We investigated whether AI support enhances editing behaviors and promotes concept learning while interacting with learner background characteristics. We analyzed 1081 revision attempts across seven topics over seven weeks from 49 undergraduates in an Introductory Physics course at a large public university. A longitudinal analytic approach using Linear Mixed-Effects Models was employed to address the research questions. Findings indicate that AI-powered formative feedback fostered revision behaviors associated with higher concept learning scores. Effective revisions occurred when students added concepts in response to AI feedback while avoiding careless deletions and surface-level sentence changes. Engagement and performance varied more by assignment than by tool proficiency, with AI scaffolds especially beneficial for students historically underperforming in STEM. These results underscore the importance of personalized feedback strategies that promote targeted revision across diverse learners.

Hyunkyu Han and Jinho Kim Elected to the 2026-2027 Board of ILSSA 🔗

September 1, 2026

Hyunkyu Han and Jinho Kim has been elected to the 2026–2027 board of the International Learning Sciences Student Association (ILSSA) within the International Society of the Learning Sciences (ISLS)!

Hyunkyu Han has been elected Asia-Pacific Regional Rep, and Jinho Kim as Co-Chair. Hyunkyu will serve a one-year term, and Jinho a two-year term.

In addition, both will represent ILSSA on other ISLS committees: Jinho Kim on the Annual Meeting Committee, and Hyunkyu on the Membership Committee.

Find out more about the ISLS committees here: https://www.isls.org/members/committees/

Jinho Kim's 2026 AI4Ed-Funded Summer Graduate Fellowship & Summit 🔗

August 20, 2026

Our graduate associate, Jinho Kim, was awarded the 2026 AI4Ed-Funded Summer Graduate Fellowship and participated in the Summit following it, held August 18-19, 2026, at the Illini Center in Chicago, IL, where she presented her team's work to advisors and peers. As part of the fellowship, Jinho Kim collaborated with two others to explore multimodal models' science diagram understanding on a benchmark of 100 diagram pairs.

The AIVO AI Institute Summer Graduate Fellowship provides a 12-week program through funding from Google.org, with fellows from five AI Institutes focused on education contributing 40 hours per week.

Edu-MLLMs: Do Multimodal Foundation Models Understand or Just Trust the Diagram?

Heather Broome, Jinho Kim, Alexander Stone

Multimodal large language models (MLLMs) are increasingly proposed as automated quality control for educational materials, yet it remains unclear whether they evaluate science diagrams with any awareness of what a diagram is meant to teach and to whom. We investigate this question with a benchmark of 100 diagram pairs drawn from the AI2D-RST corpus, in which each authentic diagram is hand-edited to introduce exactly one error, yielding pairs of correct and incorrect versions. Models (Claude Sonnet 4.6, GPT-5, Gemini 2.5 Pro, Qwen2.5-VL at 7B and 72B scales, Gemma 8B, and Ministral 8B) judge every diagram under five presentation conditions that systematically vary whether the model receives the image, a statement of the intended audience and concept, and a caption that agrees or conflicts with the image, producing structured verdicts with evidence attribution and confidence for 7,000 trials in total. This design lets us measure whether learning context improves error detection and reduces false alarms on correct diagrams, whether detection depends on an error types, and which modality a model trusts when text and image contradict each other. The addition of learning context alone did not improve the models’ judgments, and when caption and image disagreed, every frontier model trusted the text over the diagram, suggesting that current MLLMs read about diagrams more than they understand them. 
 

Check out more here:

• AIVO Grad Fellow Summit 2026 
• https://lnkd.in/p/ezkEH4eK 
• https://aiinstitutes.org/august-2026-aivo-newsletter/ 

Our lab members presented at the 2026 ISLS Annual Meeting 🔗

July 15, 2026

Our graduate research associates, Hyunkyu Han, Jinho Kim, Seora Kim, and Yoojin Bae, attended and presented at the 2026 International Society of the Learning Sciences (ISLS) Annual Meeting, held in Irvine, California, USA, from June 15–19. They presented one short paper and two posters in both in-person and virtual formats. In addition, our alumni and senior students—Lia Haddadian, Jinho, and Yoojin—served on the board of the International Learning Sciences Student Association (ILSSA). Jinho also participated as a presenter in a hybrid symposium.

In addition, Yoojin Bae was selected to participate in the Alt-Academia Career Workshop, held as part of the 2026 ISLS Annual Meeting. This selective professional development workshop, supported by the Wallace Foundation, helps learning scientists explore career pathways beyond traditional tenure-track positions. 

References

Bae, Y., Kim, J., & Kim, M. K. (2026). Four years of AI-powered formative assessment: Exploring the 2-sigma effect and fairness. In B. K. Litts, D. DeLiema, C. Lee, S. Krist, A. Mawasi, L. Martin, & K. Kumpulainen (Eds.), Proceedings of the 20th International Conference of the Learning Sciences – ICLS 2026 (pp. 1382–1386). International Society of the Learning Sciences.
 

Han, H., Kim, S., Abdeen, M. S., & Kim, M. K. (2026). AI-scaffolded summarization in STEM: How do students differ in writing and revision? In B. K. Litts, D. DeLiema, C. Lee, S. Krist, A. Mawasi, L. Martin, & K. Kumpulainen (Eds.), Proceedings of the 20th International Conference of the Learning Sciences – ICLS 2026 (pp. 2851–2853). International Society of the Learning Sciences.
 

Kim, S., Han, H., & Kim. M. (2026). Design principles for AI-supported chatbots to scaffold problem-based learning in undergraduate nursing education. In Proceedings of the 20th International Conference of the Learning Sciences – ICLS 2026 (pp. 3238–3240). International Society of the Learning Sciences.
 

Malcolm, B., Vickery, M., Louis-Strakes Lopez, J., Siciliano, L., Simon, S., Xing, G., Kim, J., Kim, C., Zhao, Y., Desai, A., Sol Gadong, E., Mabadeje, Y., Mhungu, B., Haddadian, G., Eloy, A., Soodhani, N., Prasad, R., & Bae., Y (2026). Fostering Educational Intimacy: ILSSA Intergenerational Partnerships for Purposeful Community Building. In Proceedings of the 20th International Conference of the Learning Sciences - ICLS 2026 (pp. 2424–2436). International Society of the Learning Sciences.
 

Dr. Kim delivered an invited talk at Inha University in Incheon, South Korea. 🔗

May 22, 2026

Dr. Kim was invited to a workshop series on instructional innovations offered by Inha University in Incheon, South Korea. The workshop was held via Zoom with over two hundred faculty and staff members from the university. This workshop introduced cases of AI-augmented instruction applied to higher education and adult learning. In particular, it presented educational programs and a range of application cases developed by the NSF AI Institute for Adult Learning and Online Education (AI-ALOE). The workshop also featured live demos of systems currently under development that integrate generative AI and knowledge-based AI to support hybrid instruction and simulation-based learning, along with a discussion of how these systems can be applied and scaled in real classrooms.  

Key Topics

• Introduction to AI-powered educational programs and application case studies
• A multimodal instructional platform for supporting hybrid learning
• An AI-augmented simulation platform and its application cases

Our lab members presented their research during the AI ALOE EAB meeting. 🔗

May 15, 2026

AI-ALOE held its final EAB meeting on May 15, 2026, which aimed to update the Advisory Board on the institute's accomplishments and products, showcase findings from various ALOE teams, foster networking, highlight student research, and gather insights for the NSF Annual Report and Review Meeting. The meeting was attended by our director, Dr. Kim, alongside graduate associates Ahnaf, Hyunkyu, Jinho, Seora, and Yoojin, as well as recent graduate Dr. Haddadian. Dr. Kim served as chair for the "Theories of Learning" session, where Jinho and Yoojin delivered three-minute talks on theories of learning and personalization in learning, respectively. Additionally, Ahnaf, Hyunkyu, and Seora presented posters during the lunch break.

As session chair, Dr. Kim delivered the EAB presentation "Theories of Learning," which traced how the AI-ALOE project has built and refined a theoretical foundation for one central question: how can we enhance the proficiency of adult online education across both well-structured and ill-structured learning tasks?

Jinho's talk, "Theories of Learning: Whole-Person Perspective," argued that designing equitable AI learning tools requires looking beyond standard cognitive metrics to account for a student's complete identity and life circumstances.

Yoojin's presentation, "Personalization: Five Years of SMART," explored how personalized AI feedback from the SMART agent influences student engagement during writing-based concept learning.

Seora's poster, "Design Principles for AI-Supported Chatbots to Scaffold Problem-Based Learning in Undergraduate Nursing Education," proposed a framework for developing AI chatbots tailored for nursing students.

Hyunkyu's poster presentation, "How Do Students Differ in Writing and Revision?," framed summarization as a critical learning strategy in undergraduate STEM that is often hindered by a lack of timely feedback.

Ahnaf's poster, "The Digital Chameleon: Why AI Undetectability in Writing Is a Problem for Effective Learning," explored how the rise of generative AI challenges academic integrity and trust in higher education by testing the reliability of AI-text detection tools.

Lab members attended an ALOE virtual retreat 🔗

April 2, 2026

Dr. Kim was invited to deliver one of the five AI-ALOE theme presentations during the fifth-year ALOE retreat on April 2, 2026. The presentation offered a synthesis of the project's work on theories of learning, tracing how AI-ALOE has built and refined a theoretical foundation for AI-supported adult learning over the course of the initiative. Our graduate research associates, Jinho, Yoojin, and Seora, also attended the virtual meeting.

Dr. Kim presented a talk for the NSF AI ALOE Virtual Discussions 🔗

March 16, 2026

This presentation, entitled "Contributions to Theories of Learning: Discussions on Open-Ended Problems," examines contributions to theories of learning through the lens of the SMART platform, with a particular focus on open-ended problems. It is organized around four theoretical dimensions: adult learning, which emphasizes building mental models through diverse formative supports and self-directed, personalized learning environments; cognition and knowledge, which integrates the ICAP framework (Passive, Active, Constructive, Interactive) with the Community of Inquiry model to guide instructional design decisions; personality and temperament, which draws on Self-Determination Theory, Pekrun's Control-Value Theory, and the concept of cognitive engagement to explore how motivation drives effort, persistence, and achievement; and contextual/learner background, which investigates how demographic factors such as race and first-generation status interact with engagement and performance patterns over time. Across these dimensions, the presentation connects SMART's multimodal feedback, flexible navigation, and data visualization features to measurable learning behaviors—particularly writing, revision, and review activities—offering both a theoretical grounding and empirical evidence for understanding how AI-integrated instruction can support diverse learners.

Dr. Kim presented at the Capitol. 🔗

February 19, 2026

Dr. Kim and researchers from the Byrdine F. Lewis College of Nursing and Health Professions showcased an AI simulation project as part of Georgia State University’s Research Day at the Capitol.