This paper examines the role of chatbots in supporting self-regulated learning (SRL) in higher education. Assessment as learning (AaL) emphasizes active student participation in the assessment process, encouraging learners to self-assess, reflect, and adjust their learning strategies. In this context, chatbots have emerged as a valuable tool for providing continuous, personalized feedback and fostering self-regulated learning (SRL). Utilizing natural language processing (NLP) and machine learning, chatbots can deliver real-time, adaptive responses tailored to the needs of individual learners. Chatbots can enhance metacognitive practices for graduate students by prompting reflection, monitoring progress, and guiding learning strategies. This study explores the theoretical framework of AaL within constructivist and SRL paradigms, analyzing how chatbots can support and transform assessment tasks into learning tasks in accordance with the philosophy of AaL. It also discusses the potential of chatbots to facilitate reflective practices and provide immediate feedback, helping students adjust their approaches to complex tasks. Key benefits of chatbot-assisted AaL include the ability to offer real-time feedback, support continuous learning, and promote independent, reflective learning behaviors. Despite these advantages, challenges remain. The accuracy of chatbot responses and over-reliance on AI-based assessment tools are concerns that need to be addressed. The paper emphasizes the importance of balancing chatbot use with human feedback, particularly in graduate education, where mentorship plays a crucial role.
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