SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification
  and LLM Assistance
Self-regulated learning (SRL) is crucial for college students navigating
increased academic demands and independence. Insufficient SRL skills can lead
to disorganized study habits, low motivation, and poor time management,
undermining learners ability to thrive in challenging environments. Through a
formative study involving 59 college students, we identified key challenges
students face in developing SRL skills, including difficulties with
goal-setting, time management, and reflective learning. To address these
challenges, we introduce SRLAgent, an LLM-assisted system that fosters SRL
skills through gamification and adaptive support from large language models
(LLMs). Grounded in Zimmermans three-phase SRL framework, SRLAgent enables
students to engage in goal-setting, strategy execution, and self-reflection
within an interactive game-based environment. The system offers real-time
feedback and scaffolding powered by LLMs to support students independent study
efforts. We evaluated SRLAgent using a between-subjects design, comparing it to
a baseline system (SRL without Agent features) and a traditional multimedia
learning condition. Results showed significant improvements in SRL skills
within the SRLAgent group (p < .001, Cohens d = 0.234) and higher engagement
compared to the baselines. This work highlights the value of embedding SRL
scaffolding and real-time AI support within gamified environments, offering
design implications for educational technologies that aim to promote deeper
learning and metacognitive skill development.
68504ea12932e11c891b5871
Wentao Ge
claimed_verified
2025-06-17T07:22:12.913Z
6465995994327a238f5a4f03
/avatars/e8e29603977b8ac2ddf62b20bd97a8f2.svg
Ge Wentao
user
Owenngt
68504ea12932e11c891b5872
Yuqing Sun
68504ea12932e11c891b5873
Ziyan Wang
68504ea12932e11c891b5874
Haoyue Zheng
68504ea12932e11c891b5875
Weiyang He
68504ea12932e11c891b5876
Piaohong Wang
68504ea12932e11c891b5877
Qianyu Zhu
68504ea12932e11c891b5878
Benyou Wang
SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification
  and LLM Assistance
Self-regulated learning (SRL) is crucial for college students navigating
increased academic demands and independence. Insufficient SRL skills can lead
to disorganized study habits, low motivation, and poor time management,
undermining learners ability to thrive in challenging environments. Through a
formative study involving 59 college students, we identified key challenges
students face in developing SRL skills, including difficulties with
goal-setting, time management, and reflective learning. To address these
challenges, we introduce SRLAgent, an LLM-assisted system that fosters SRL
skills through gamification and adaptive support from large language models
(LLMs). Grounded in Zimmermans three-phase SRL framework, SRLAgent enables
students to engage in goal-setting, strategy execution, and self-reflection
within an interactive game-based environment. The system offers real-time
feedback and scaffolding powered by LLMs to support students independent study
efforts. We evaluated SRLAgent using a between-subjects design, comparing it to
a baseline system (SRL without Agent features) and a traditional multimedia
learning condition. Results showed significant improvements in SRL skills
within the SRLAgent group (p < .001, Cohens d = 0.234) and higher engagement
compared to the baselines. This work highlights the value of embedding SRL
scaffolding and real-time AI support within gamified environments, offering
design implications for educational technologies that aim to promote deeper
learning and metacognitive skill development.
A gamified LLM-assisted system, SRLAgent, significantly improves self-regulated learning skills in college students through interactive, goal-setting, and real-time AI feedback.
LLM-assisted system gamification adaptive support large language models SRL (Self-regulated learning) Zimmermans three-phase SRL framework goal-setting strategy execution self-reflection between-subjects design baseline system traditional multimedia learning condition