Findings showed that students utilized the TA-AI as a strategic safety net, and a “sweet spot” in quiz difficulty was identified where the TA-AI is most effective in activating metacognitive prompting.
Abstract
This study explores the integration of a multi-agent AI system designed to support self-regulated learning (SRL) in computer science education. Building on preliminary work that introduced a Teaching Assistant AI (TA-AI) for real-time scaffolding, an Analytics-AI for pattern recognition, and an AI-Improver for iterative refinement, this study evaluates the system’s empirical impact on student help-seeking behavior. Guided by fundamental SRL principles, this study analyzed data from 38 graduate students across 109 help-seeking attempts to investigate the factors influencing prompting strategies during system implementation. Findings showed that students utilized the TA-AI as a strategic safety net. Questions where students sought help were associated with significantly lower correctness (OR = 0.35, p < .001). A “sweet spot” in quiz difficulty was identified where the TA-AI is most effective in activating metacognitive prompting: medium-difficulty questions significantly increased the likelihood of
Artificial Intelligence can be a potent supplementary instrument for the development of self-confidence and communication skills, provided that it is conducted through ethical, human-assisted learning.
Shalika Grace Das, Purnima Sharma, N. Saxena et al.· Journal of Asia Entrepreneur...· 0 citations
The popularisation of artificial intelligence assisted learning tools in higher education gives new technical circumstances for the promotion of college students’ autonomous learning ability. This research studies the enabling mechanism and possible risks of AI-assisted learning for autonomous learning development from...
Zi-Ye Zhang· International Journal of Edu...· 0 citations
It is concluded that while AI-driven scaffolded feedback holds significant promise for enhancing learning in K-8 classrooms, further research is needed to explore its long-term effects and to develop evidence-based strategies for effective implementation, particularly at the elementary school level.
Connie Ngujo, Precious Albao, Regina P. Galigao· International journal of hum...· 0 citations
Purposes: The rapid integration of artificial intelligence (AI) in education has reinforced techno-optimistic assumptions that algorithmic efficiency can support increasingly autonomous learning. This study examined Generation Z students’ preferences for AI-assisted and teacher-assisted mathematics learning through th...
F. Arwadi, T. Lam, Nur Qalbi· Al-Jabar: Jurnal Pendidikan...· 0 citations
The integration of artificial intelligence (AI) in education has transformed learning environments into data-driven instructional decision support systems (DSS). However, system effectiveness is contingent not only on technical capability but on how users interpret, evaluate, and implement AI-generated outputs. Grounde...
M. Sasmitha, Sasmoko, Viga Laksa Hardjanto et al.· International Conferences on...· 0 citations
Background The rise of artificial intelligence systems that can make their own goals plan several steps and take initiative in tasks that involve people is changing how students learn math. Earlier technology was mostly a tool thatwaited for instructions. Currently, agentic AI systems are becoming increasingly popular....
Ruhit Bardhan, Suman Das, G. A. Ganati et al.· F1000Research· 0 citations
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