Harnessing AI and Generative AI to Improve Students Psycho-Affective Traits and Their Learning Outcomes: From Theory to Practice
细分领域
Theoretical Frameworks and Models
Integrating SDT, reinforcement theory, and associative learning principles with AI-driven educational tools to explain how these technologies can satisfy psychological needs and foster intrinsic motivation.
Development of conceptual models to elucidate AI’s role in supporting cognitive, emotional, and motivational dimensions of learning.
Studies that explore the alignment between AI applications and existing theories of cognitive, emotional, and social engagement.
Generative AI for Personalized Emotional and Motivational Support
AI-driven tools for personalized feedback that enhances student motivation, emotional resilience, and growth mindset.
Adaptive learning systems leveraging generative AI to tailor experiences that foster students’ self-regulation and persistence, grounded in reinforcement theory and associative learning concepts.
Case studies illustrating practical implementations of AI that support students’ emotional growth and self-concept development.
AI-Enhanced Engagement and Well-being in Education
Applications of AI to create environments that meet students' needs for autonomy, competence, and relatedness, as outlined in SDT.
Investigating the role of AI in reducing student stress and fostering engagement through gamification, immersive experiences, and social collaboration.
Practical applications and empirical studies of AI-enhanced well-being, emphasizing long-term benefits for emotional and psychological health.
Affective Computing and Emotional Analytics in Education
Advances in affective computing to detect and support students’ emotional states in real-time, providing tailored interventions to maintain academic engagement.
Ethical considerations surrounding the collection and use of affective data in educational settings, with implications for student privacy and agency.
Techniques for using emotional analytics to provide real-time, individualized support, aligning with reinforcement theory to encourage positive learning behaviors.
Generative AI and Classroom Dynamics
AI’s role in promoting collaborative learning and peer support in diverse educational contexts, including its potential to foster positive classroom dynamics and social skills.
Examination of how AI can facilitate student interactions and engagement by addressing social and emotional learning (SEL) needs.
Case studies on AI-facilitated collaborative projects and their impact on students’ engagement, motivation, and emotional resilience.
Future Directions in AI-Enhanced Education
Perspectives on the integration of AI to support holistic educational practices that prioritize psycho-affective traits alongside academic skills.
Challenges, ethical considerations, and limitations of implementing AI-driven models for psycho-affective development.
