Enhancing Learners' Self-Assessment through AI-Based Reflection Tools

The integration of artificial intelligence (AI) in education has shown promise in personalizing learning experiences and fostering problem-solving skills (Smith & Johnson, 2020). However, limited research has explored the impact of AI-driven tools on learners' self-assessment, a crucial element of metacognitive development and autonomous learning (Brown et al., 2019). This study addresses this gap by developing and evaluating AI-based learning cards designed to enhance self-assessment and promote independent learning behaviors. This research employs a mixed-methods approach to evaluate the effectiveness of AI-based learning cards. A quantitative survey was conducted with secondary school students to measure changes in self-assessed problem-solving abilities before and after using the AI tools. Additionally, semi-structured interviews provided qualitative insights into students' experiences and perceptions of these tools. Quantitative data were analyzed using paired t-tests, while thematic analysis was applied to qualitative data to identify recurring patterns and themes. Results reveal a significant improvement in learners' self-assessment capabilities, as evidenced by higher self-assessment scores and enhanced metacognitive skills. Students reported improved goal-setting abilities and a stronger inclination towards autonomous learning. Qualitative findings further validated these outcomes, with participants highlighting the role of AI in fostering reflective thinking and identifying gaps in knowledge. The immediate, personalized feedback provided by the AI tools was a critical factor in supporting self-directed learning and enabling learners to manage their pace and priorities effectively. This study underscores the transformative potential of AI-based learning tools in shifting the focus of learning from teacher-led instruction to learner-driven autonomy. By fostering self-regulated learning environments, AI tools can empower students to take control of their educational journey. The findings contribute to the growing body of literature on AI in education and suggest avenues for future research, including long-term studies on self-regulated learning and the adaptation of AI tools to diverse educational settings.