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<p>Could AI be used to write caring, affective-motivational messages? In the current case study, generative AI (GPT-3) was used to enhance periodic feedback practices with personalized affective-motivational messages for half of the learners enrolled in a digital fabrication course, a challenging learning environment with high emotional needs. Human instructors used the platform to co-create and revise messages generated based on learner data, namely self-reports on key affective-motivational states and weekly blog post assignments. Findings from this small course setting point to the possibility that AI-augmented feedback may play a role in supporting learner self-efficacy, sense of belonging, and burnout. Results of qualitative inquiries involving cued recall and possible futures also suggest that in this setting, effects of the feedback were mediated through warmer perceived classroom climate, rather than by directly triggering adaptive behaviors. Based on findings, we suggest generative AI may best support learner motivation and affect by taking on the roles of <i>warm tone-setter</i>, <i>deferential aide</i>, and <i>mediator for human connections</i>, and present implications for designing affective-motivational supports with AI.</p>

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Using AI to Care: Lessons Learned from Leveraging Generative AI for Personalized Affective-Motivational Feedback

  • 期刊论文

  • 发表日期：

    2025年1月15日

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  • Gahyun Sung,
  • Léonore Guillain,
  • Bertrand Schneider

摘要

Could AI be used to write caring, affective-motivational messages? In the current case study, generative AI (GPT-3) was used to enhance periodic feedback practices with personalized affective-motivational messages for half of the learners enrolled in a digital fabrication course, a challenging learning environment with high emotional needs. Human instructors used the platform to co-create and revise messages generated based on learner data, namely self-reports on key affective-motivational states and weekly blog post assignments. Findings from this small course setting point to the possibility that AI-augmented feedback may play a role in supporting learner self-efficacy, sense of belonging, and burnout. Results of qualitative inquiries involving cued recall and possible futures also suggest that in this setting, effects of the feedback were mediated through warmer perceived classroom climate, rather than by directly triggering adaptive behaviors. Based on findings, we suggest generative AI may best support learner motivation and affect by taking on the roles of warm tone-setter, deferential aide, and mediator for human connections, and present implications for designing affective-motivational supports with AI.