Emotionally Enriched Feedback via Generative AI (2410.15077v1)

Abstract: 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.

• The paper shows that emotionally enriched AI feedback significantly reduced negative emotions, such as anger, in a randomized controlled trial with 425 first-year engineering students.
• It employs a robust methodology based on the Control-Value Theory, comparing enriched feedback with neutral feedback to assess emotional responses and engagement.
• Results indicate that while enriched feedback improves students' perception of usefulness, it does not directly boost academic work quality.

Emotionally Enriched Feedback via Generative AI: Insights and Implications

The paper titled "Emotionally Enriched Feedback via Generative AI" explores the integration of emotional elements in AI-driven feedback mechanisms within educational settings. This research addresses a critical aspect of digital education—enhancing student engagement and emotional well-being through technologically mediated feedback. By utilizing a randomized controlled experiment with 425 first-year engineering students, the researchers investigate the effects of emotionally enriched feedback generated by LLMs on student emotional responses and engagement levels.

Summary of Research Findings

The paper evaluates the impact of AI feedback supplemented with motivational components such as praise and visual aids on various aspects of student engagement and emotion regulation. Building on the Control-Value Theory of Achievement Emotions (CVT), the experiment compares outcomes between an experimental group receiving enriched feedback and a control group receiving neutral feedback.

Emotional Responses and Engagement:

• The experimental group demonstrated reduced negative emotions, notably anger, when interacting with feedback, suggesting potential emotional benefits of enriched feedback.
• Despite the reduction in negative emotions, the paper found no significant difference in overall feedback engagement or academic work quality between the groups.

Perceived Usefulness:

• Emotionally enriched feedback was rated as more beneficial by students. This highlights the potential of such feedback to impact students' perceptions, making AI an effective tool for feedback delivery in educational settings.

Academic Outcome:

• The quality of student work remained unaffected by the feedback type, indicating that while emotional elements enrich the feedback experience, they do not directly enhance academic performance.

Theoretical and Practical Implications

This research adds to the discourse on educational technology by emphasizing the importance of considering emotional dimensions when implementing AI feedback systems in educational environments. Importantly, the findings underscore the necessity of balancing cognitive and emotional factors to optimize learning experiences.

Theoretical Implications:

1. Integration with CVT: The paper successfully utilizes CVT to explore emotional dimensions of AI feedback, suggesting the theory's robustness in digital learning contexts. These insights can guide further research into emotional scaffolding in educational technologies.
2. Feedback Engagement Dynamics: The intricate interplay between negative emotions and cognitive engagement is highlighted. Emotionally intelligent feedback may reduce cognitive overload and increase receptiveness, contributing to theoretical models of feedback mechanisms.

Practical Implications:

1. Designing AI Feedback Systems: The paper suggests guidelines for enriching AI-mediated feedback with emotional content to enhance student engagement, potentially improving digital learning environments that lack physical emotional cues.
2. Broadening AI Application in Education: Educators and technologists might explore application areas where emotional enrichment can foster a supportive learning atmosphere, which could be imperative in online and remote learning settings.
3. Long-term Emotional Well-being: Incorporating emotional elements can improve students' emotional health, crucial for maintaining motivation and reducing attrition in online education platforms.

Future Research Directions

The findings indicate several avenues for further research, including:

• Longitudinal Studies: Investigating the long-term effects of emotionally enriched feedback on academic outcomes would provide comprehensive insights into its effectiveness.
• Adaptive Feedback Systems: Enhancing AI algorithms to better adjust to students’ immediate emotional and cognitive needs could optimize feedback effectiveness.
• Diverse Educational Contexts: Testing the applicability of these findings across different subjects and educational levels may yield insights into the universality of emotionally enriched feedback strategies.

This research builds a foundation for further exploration into integrating emotional intelligence within AI educational tools. By addressing both cognitive and emotional dimensions, future developments in AI can potentially cultivate a more holistic and effective educational environment.