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A nationwide study on generative AI knowledge, motivation, and emotional responses in predicting students’ perceived need for AI education

Seyoung Lee et al.

Telematics and Informatics2026https://doi.org/10.1016/j.tele.2026.102372article
ABDC C
Weight
0.50

What the paper says

• Generative AI knowledge increases intrinsic and extrinsic motivation • Motivation enhances users’ emotional responses toward AI. • Emotional responses predict the perceived need for GenAI education. • Serial mediation through motivation and emotion explains educational needs. The use of generative AI (GenAI) technologies, such as ChatGPT, Midjourney, and Claude, is rapidly expanding across education, research, and various industries, becoming an indispensable tool in everyday life. However, timely and adequate education is necessary for the effective and ethical utilization of such technologies. This study examines users’ psychological mechanisms behind GenAI knowledge and education by incorporating a knowledge-motivation-emotion-education pathway and provides practical implications for educational institutions. To that end, this study analyzed a nationwide sample of 3,959 middle and high school students who participated in a survey. The results revealed that GenAI knowledge significantly increased intrinsic and extrinsic motivation, which in turn increased the degree of emotional response. The results also supported the notion that emotional responses positively predict the perceived need for GenAI education. Moreover, the results revealed a significant indirect relationship between GenAI knowledge and educational needs through intrinsic motivation and emotional responses both individually and serially. The individual mediation of extrinsic motivation between GenAI knowledge and educational needs was not significant; however, serial mediation through extrinsic motivation and emotional responses was significant.

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https://doi.org/https://doi.org/10.1016/j.tele.2026.102372

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@article{seyoung2026,
  title        = {{A nationwide study on generative AI knowledge, motivation, and emotional responses in predicting students’ perceived need for AI education}},
  author       = {Seyoung Lee et al.},
  journal      = {Telematics and Informatics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.tele.2026.102372},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.