Emotionally Enriched Feedback via Generative AI
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917809872699392 |
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| author | Alsaiari, Omar Baghaei, Nilufar Lahza, Hatim Lodge, Jason Boden, Marie Khosravi, Hassan |
| author_facet | Alsaiari, Omar Baghaei, Nilufar Lahza, Hatim Lodge, Jason Boden, Marie Khosravi, Hassan |
| contents | This study investigates the impact of emotionally enriched AI feedback on student engagement and emotional responses in higher education. Leveraging the Control-Value Theory of Achievement Emotions, we conducted a randomized controlled experiment involving 425 participants where the experimental group received AI feedback enhanced with motivational elements, while the control group received neutral feedback. Our findings reveal that emotionally enriched feedback is perceived as more beneficial and helps reduce negative emotions, particularly anger, towards receiving feedback. However, it had no significant impact on the level of engagement with feedback or the quality of student work. These results suggest that incorporating emotional elements into AI-driven feedback can positively influence student perceptions and emotional well-being, without compromising work quality. Our study contributes to the growing body of research on AI in education by highlighting the importance of emotional considerations in educational technology design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emotionally Enriched Feedback via Generative AI Alsaiari, Omar Baghaei, Nilufar Lahza, Hatim Lodge, Jason Boden, Marie Khosravi, Hassan Human-Computer Interaction This study investigates the impact of emotionally enriched AI feedback on student engagement and emotional responses in higher education. Leveraging the Control-Value Theory of Achievement Emotions, we conducted a randomized controlled experiment involving 425 participants where the experimental group received AI feedback enhanced with motivational elements, while the control group received neutral feedback. Our findings reveal that emotionally enriched feedback is perceived as more beneficial and helps reduce negative emotions, particularly anger, towards receiving feedback. However, it had no significant impact on the level of engagement with feedback or the quality of student work. These results suggest that incorporating emotional elements into AI-driven feedback can positively influence student perceptions and emotional well-being, without compromising work quality. Our study contributes to the growing body of research on AI in education by highlighting the importance of emotional considerations in educational technology design. |
| title | Emotionally Enriched Feedback via Generative AI |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.15077 |