Advancements in AI-Supported Learning

The field of AI-supported learning is rapidly evolving, with a growing focus on developing innovative strategies to enhance student engagement, improve learning outcomes, and foster effective human-AI collaboration. Recent studies have explored the potential of large language models (LLMs) to support learning, particularly in areas such as programming education, writing, and self-regulated learning. Notably, research has highlighted the importance of instructional design, pedagogical prompting, and explainable feedback in unlocking the full potential of AI-supported learning tools. Furthermore, the development of theory-driven learning analytics dashboards and metacognitive scaffolding frameworks has shown promise in promoting deeper learning, enhancing conceptual understanding, and developing transferable skills. Overall, the field is moving towards a more nuanced understanding of how AI can be leveraged to support student learning, with a growing emphasis on designing AI-powered tools that complement human instruction and foster more effective learning outcomes. Noteworthy papers include: 'From Generation to Adaptation: Comparing AI-Assisted Strategies in High School Programming Education', which introduces a dual-scaffolding model for effective LCA integration; 'When learning analytics dashboard is explainable: An exploratory study on the effect of GenAI-supported learning analytics dashboard', which demonstrates the significance of explainable feedback in fostering deeper learning; and 'Irec: A Metacognitive Scaffolding for Self-Regulated Learning through Just-in-Time Insight Recall', which proposes a novel paradigm for promoting self-regulated learning through context-triggered insight recall.

Sources

From Generation to Adaptation: Comparing AI-Assisted Strategies in High School Programming Education

When learning analytics dashboard is explainable: An exploratory study on the effect of GenAI-supported learning analytics dashboard

LLM-Generated Feedback Supports Learning If Learners Choose to Use It

Can AI support student engagement in classroom activities in higher education?

Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education

FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring

Can theory-driven learning analytics dashboard enhance human-AI collaboration in writing learning? Insights from an empirical experiment

Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning

Integrating Pair Programming as a Work Practice

Exploring Developer Experience Factors in Software Ecosystems

Irec: A Metacognitive Scaffolding for Self-Regulated Learning through Just-in-Time Insight Recall: A Conceptual Framework and System Prototype

Enhancing Programming Pair Workshops: The Case of Teacher Pre-Prompting

That's Not the Feedback I Need! -- Student Engagement with GenAI Feedback in the Tutor Kai

AI in the Writing Process: How Purposeful AI Support Fosters Student Writing

CogGen: A Learner-Centered Generative AI Architecture for Intelligent Tutoring with Programming Video

KOALA: a Configurable Tool for Collecting IDE Data When Solving Programming Tasks

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