Ahnaf Niloy co-authored and presented a paper at the 2026 AIME-Con.
October 5, 2026
Ahnaf Niloy, a Learning Sciences Ph.D. student and our graduate research assistant, co-authored a conference paper with Golnoush Haddadian, a Learning Sciences graduate and postdoctoral researcher in the Department of Learning Sciences, and Dr. Min Kyu Kim. He presented the study at the Artificial Intelligence in Measurement and Education Conference (AIME-Con) 2026, held October 5-7 in Pittsburgh, Pennsylvania.
Niloy presented his co-authored paper, “Who Keeps Up with Whom? Assessing GenAI Text Detection Across Version Updates,” challenging the reliability of commercial AI detection tools in an era of rapidly evolving generative AI. Drawing on an empirical analysis of 960 text samples, the study examined how chatbot and detector updates, AI humanization techniques, and writing styles influence detection accuracy. The findings suggest that generative AI chatbots are advancing faster than detection technologies can reliably keep pace, raising concerns about the accuracy of widely used tools such as Turnitin and GPTZero. The research highlights the potential consequences of unreliable AI detection for academic integrity, particularly the risk of misidentifying student work. Beyond identifying these limitations, the study advocates for evidence-based institutional policies and improved detection strategies to promote fair, responsible, and trustworthy AI integration in education.
The research is published as a conference proceeding which can be accessed via the following link:
Who Keeps Up with Whom? Assessing GenAI Text Detection Across Version Updates - ACL Anthology
To cite the work, the following citation format is recommended for APA 7th:
Niloy, A. C., Haddadian, G., & Kim, M. K. (2026). Who keeps up with whom? Assessing GenAI text detection across version updates. In J. Wilson, C. Ormerod, & M. Beiting-Parrish (Eds.), Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full papers (pp. 560–570). National Council on Measurement in Education. https://aclanthology.org/2026.aimecon-main.63/
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/