AI in Education: Enhancing Learning Through Active Engagement and Self-Regulation

The research landscape in the intersection of artificial intelligence and education is rapidly evolving, with a particular focus on leveraging AI to enhance learning processes and outcomes. A significant trend is the integration of large language models (LLMs) into qualitative analysis, particularly in fields like software engineering, where their potential to streamline tasks such as coding and thematic analysis is being explored. However, the nuanced and context-dependent nature of qualitative data necessitates careful consideration of ethical implications and the continued role of human expertise in interpretation.

In the realm of student writing, generative AI is being utilized to support cognitive tasks, with studies highlighting the importance of active engagement with AI-generated content. Modifying AI outputs is shown to significantly enhance essay quality, suggesting that meaningful cognitive engagement is crucial for learning outcomes. Conversely, passive acceptance of AI-generated text can lead to decreased quality, underscoring the need for pedagogical strategies that encourage higher-order thinking.

Self-regulated learning (SRL) processes are also under scrutiny, with advancements in trace-based measures providing deeper insights into how students at different educational levels manage their learning. Secondary education students exhibit distinct SRL patterns compared to their higher education counterparts, indicating a need for targeted interventions to develop SRL skills.

Lastly, the concept of hybrid intelligence, where learners interact with various support agents including AI, is emerging as a critical area of study. Research indicates that while AI can enhance performance, it may also lead to metacognitive laziness, emphasizing the balance between leveraging technology and maintaining active learning strategies.

Noteworthy papers include one that demonstrates the impact of modifying AI-generated text on essay quality, and another that reveals the potential for AI to promote metacognitive laziness.

Sources

Applications and Implications of Large Language Models in Qualitative Analysis: A New Frontier for Empirical Software Engineering

Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality

Self-regulated Learning Processes in Secondary Education: A Network Analysis of Trace-based Measures

Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance

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