Yu-Feng Lan

Department of Information Management, National Formosa University, YunLin, Taiwan, R.O.C. // yflan@nfu.edu.tw

ABSTRACT: 

This study investigates how AI-mediated debugging scaffolds can be structurally embedded within automated assessment systems to reorganize novice learners’ problem-solving processes beyond correctness-based feedback. Instructor-authored programming problems were transformed into three adaptive formats: faulty code snippets, line-reordering tasks, and graduated hints. A five-week quasi-experimental study with 90 undergraduates compared a conventional automated assessment environment with an AI-augmented version incorporating debugging-oriented scaffolding. Learners in the AI-augmented condition achieved significantly higher programming performance and more positive attitudes, with particularly pronounced gains among low-achieving students. Log analyses showed differentiated interaction patterns, suggesting that frequent scaffold engagement reflected sustained effort under conceptual difficulty rather than simple performance deficiency. Interview data converged with these results, indicating enhanced confidence, improved error diagnosis, and more reflective reasoning. Conceptually, the findings position AI-generated scaffolding as both cognitive support and an interactional mechanism shaping learner–system engagement in automated assessment environments, supporting process-oriented and learner-centered assessment design.

Keywords: 

AI-generated debugging, Automated assessment, Adaptive scaffolding, Computer science education, Novice learners

Prajakt Pande

Aarhus University, Denmark // prajakt@au.dk

Morten Erik Moeller

University College Copenhagen, Denmark // moem@kp.dk

Biljana Mojsoska

Roskilde University, Denmark // biljana@ruc.dk

ABSTRACT: 

Immersive virtual reality (VR) affords novel sensorimotor experiences that can support embodied learning of complex STEM concepts requiring learners to imagine spatial and dynamic phenomena. However, our understanding of how to harness VR’s unique affordances to systematically implement embodied learning design principles, and how learners’ embodied interactions relate to learning, is still evolving. In this paper, we present the design and development of I, Enzyme, a VR simulation environment conceptualized to foster embodied and enactive learning of complex organic chemistry mechanisms in a biologically grounded enzymatic catalysis narrative. The environment integrates embodied interaction design, phenomenological inquiry, and multimodal learning analytics (interaction- and eye-tracking). We also report findings from a single-group pre–post study involving 41 undergraduates that investigated how learners’ interaction behavior, gaze patterns, and sense of embodiment during simulation use relate to their conceptual understanding of organic reaction mechanisms. We found significant overall pre–post gains in conceptual understanding, intrinsic motivation, and self-efficacy, as well as substantial differences between higher- and lower-gain learners in interaction metrics such as frequency and timing, but not in gaze behavior. The paper contributes to the literature on embodied and immersive learning technology design, and illustrates how multimodal analytics can reveal learning-relevant embodied engagement in VR environments, while also pointing to current methodological constraints of eye-tracking in dynamic immersive settings.

Keywords: 

Virtual reality, Embodied learning, Science education, Multimodal analytics, Eye tracking

Fang-ying Lo

Center for General Education, Asia University, Taiwan // flo@asia.edu.tw

Ya-Tsen Lin

Department of Computer Science & Information Engineering, National Central University, Taiwan // tina30124@gmail.com

Chen-Chung Liu

Department of Computer Science & Information Engineering, National Central University, Taiwan // ccliu@cl.ncu.edu.tw

ABSTRACT: 

Prior empirical studies and systematic reviews have examined interactive reading interventions, including teacher-led dialogic interactions and agent-mediated dialogue, and have reported benefits for learners’ comprehension support and reading engagement through questioning, discussion, and feedback. While tutor agents enabled by generative artificial intelligence (GenAI) have demonstrated efficacy, they often position students in a role to passively answer questions, which may limit learners’ autonomy. This study addressed this gap by exploring tutee agent design, powered by GenAI, to enhance students’ reading interest within the theoretical framework of interest-driven creation (IDC). Within this framework, a tutee agent, acting as a student, aimed to prompt learners to engage in the creation of their own tutee agent by narrative retells, while a tutor agent supported learners’ comprehension. Our study, involving 76 elementary students, compared agent-assisted IDC reading with traditional teacher-assisted and independent reading. Findings supported the positive effects of agent-assisted IDC reading in enhancing students’ interest and flow perception. Notably, interactions with the tutee agent exhibited a positive correlation with reading interest, suggesting that the active process of learning by teaching constitutes a more impactful engagement mechanism than the passive reception of input from tutor agents. Implications of the findings are discussed.

Keywords: 

Interest-driven creation (IDC) theory, Learning by teaching, Agent technology, Generative AI, Reading

Yao-Ting Sung

Department of Educational Psychology and Counseling, National Taiwan Normal University, Taiwan // sungtc@cc.ntnu.edu.tw

Chien-Chih Tseng

Department of Educational Psychology and Counseling, National Taiwan Normal University, Taiwan // s0902041@gmail.com

Tsui-Chun Hu

College of Education, National Taiwan Normal University, Taiwan // tsuichun@ntnu.edu.tw

ABSTRACT: 

With the advancement of adaptive and data-driven technologies, adaptive reading systems have gained increasing attention in educational contexts. However, essential design components for fostering individualized and independent reading remain insufficiently specified. To address this gap, the present study developed and evaluated a Chinese adaptive reading system—the SmartReading (SR) platform—grounded in an integrative framework combining personalized learning and phase-based self-regulated learning (SRL). At the system level, the platform integrates three interrelated components: (a) diagnostic assessment of reading ability, (b) calibrated text recommendation aligned with learners’ proficiency, and (c) structured feedback and reading portfolios designed to support SRL. Using a randomized field trial, 857 fifth-graders and 891 seventh-graders participated in a year–long experiment. The experimental groups used the SR platform weekly alongside their regular Mandarin classes, completing at least six reading tasks, while the control groups continued with regular Mandarin instruction. The results showed that the experimental groups participating in the SR program significantly increased the proportion of students reaching the norm of reading proficiency. Moreover, the reading abilities of students in the experimental group were significantly higher than those in the control group after participating in the SR program for both grades. These findings suggest that adaptive systems grounded in SRL-informed design may function as co-regulatory learning architectures, in which aspects of regulation are shared across learners and adaptive technologies, thereby supporting sustained engagement and reading comprehension in authentic school settings.

Keywords: 

Adaptive reading platform, Independent reading, Self-regulated learning, Text readability, Reading comprehension