TY - GEN
T1 - Concept Maps as Metacognitive Scaffolds
T2 - 14th International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2025
AU - Zhang, Cuilian
AU - Hu, Xiao
AU - Wei, Wei
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Current Generative AI (GenAI)-based academic counseling systems suffer from some limitations including passive response modes, and the inability to promote systematic exploration of complex decision factors. Students frequently struggle with formulating effective queries and lack structured approaches to articulate their multifaceted academic needs, resulting in superficial consultations. This study investigates how integrating concept maps as metacognitive scaffolding tools can transform student interactions with GenAI systems, shifting from passive information retrieval to active knowledge construction.We conducted a quasi-experimental study with 120 participants from a leading polytechnic university in mainland China, comparing GenAI-only consultations (n=60) with concept map-enhanced GenAI interactions (n=60). Factor occurrence analysis and Epistemic Network Analysis (ENA) examined motivational patterns across five dimensions: Academic Development, Career-Driven, Interest-Driven, Emotion-Driven, and Habit-Driven factors.Results demonstrate that concept map integration makes visible decision-making considerations. While traditional academic factors remained important, concept map users exhibited significantly enhanced career orientation and emotional self-awareness. Critically, these students demonstrated heightened metacognitive awareness through increased articulation of uncertainties (e.g., 'no plans', 'no career goals'), indicating deeper self-reflection. ENA analysis revealed denser network connectivity with robust cross-dimensional linkages between academic requirements and career preparation.The study establishes that concept maps serve as cognitive bridges, transforming GenAI interactions from reactive consultations to proactive exploration. These findings demonstrate that visual metacognitive tools can improve both depth and breadth of student-AI interactions while promoting self-regulated decision-making capabilities, supporting the "internalization of external regulation"process described in Self-Determination Theory.
AB - Current Generative AI (GenAI)-based academic counseling systems suffer from some limitations including passive response modes, and the inability to promote systematic exploration of complex decision factors. Students frequently struggle with formulating effective queries and lack structured approaches to articulate their multifaceted academic needs, resulting in superficial consultations. This study investigates how integrating concept maps as metacognitive scaffolding tools can transform student interactions with GenAI systems, shifting from passive information retrieval to active knowledge construction.We conducted a quasi-experimental study with 120 participants from a leading polytechnic university in mainland China, comparing GenAI-only consultations (n=60) with concept map-enhanced GenAI interactions (n=60). Factor occurrence analysis and Epistemic Network Analysis (ENA) examined motivational patterns across five dimensions: Academic Development, Career-Driven, Interest-Driven, Emotion-Driven, and Habit-Driven factors.Results demonstrate that concept map integration makes visible decision-making considerations. While traditional academic factors remained important, concept map users exhibited significantly enhanced career orientation and emotional self-awareness. Critically, these students demonstrated heightened metacognitive awareness through increased articulation of uncertainties (e.g., 'no plans', 'no career goals'), indicating deeper self-reflection. ENA analysis revealed denser network connectivity with robust cross-dimensional linkages between academic requirements and career preparation.The study establishes that concept maps serve as cognitive bridges, transforming GenAI interactions from reactive consultations to proactive exploration. These findings demonstrate that visual metacognitive tools can improve both depth and breadth of student-AI interactions while promoting self-regulated decision-making capabilities, supporting the "internalization of external regulation"process described in Self-Determination Theory.
KW - Concept maps
KW - Generative AI
KW - Metacognitive scaffolding
UR - https://www.scopus.com/pages/publications/105033218339
U2 - 10.1109/TALE66047.2025.11346728
DO - 10.1109/TALE66047.2025.11346728
M3 - Conference contribution
AN - SCOPUS:105033218339
T3 - TALE 2025 - 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Proceedings
BT - TALE 2025 - 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 December 2025 through 7 December 2025
ER -