Enhancing Formative Assessment and Feedback in Collaborative Inquiries with an AI-powered Analytical Module
Collaborative inquiry is one of the most prevalent learning approaches in higher education. It involves students working together through sustained communication to solve problems and construct knowledge. Formative assessment and feedback are vital for developing students’ higher-order thinking skills, especially problem-solving and critical thinking, within collaborative inquiries. However, university instructors struggle to assess the status of collaborative inquiries efficiently and provide feedback to students effectively due to increasing class sizes and the vast amounts of discourse data generated. This struggle further diminishes the quality of student learning. In this context, this project introduces an innovative solution in the form of an easy-to-use and user-friendly analytical module powered by artificial intelligence (AI). Guided by the community of inquiry (CoI) theoretical model, this analytical module automatically processes discourse data, models students’ inquiry patterns, and generates visualizations and explanations to inform instructors’ decision-making. This analytical module holds great potential in assisting and enhancing instructors’ formative assessment and feedback practices. During the project period, the analytical module will directly impact 200 to 300 undergraduate and graduate students in several inquiry-based courses (e.g., HPI 2001 Honours Project, CPI 2001 Capstone Project, and PRJ6003 Practitioner-based Research Project). Quasi-experiments will be conducted to explore the effects of AI-assisted formative assessment and feedback on students’ inquiry patterns and perceived feedback effectiveness. In addition, 30 to 50 staff members will benefit from attending workshops and seminars on this project’s design and findings.In the long term, this project aims to extend the reach of the proposed analytical module to all inquiry-based courses in EdUHK and beyond, optimizing the way collaborative inquiries are assessed and feedback is provided. The outcomes and findings of this project will not only provide a sustainable and generalizable path for incorporating AI into teaching and learning processes but also significantly enhance students' achievement of intended learning outcomes, especially problem-solving and critical thinking skills.
Project Start Year: 2024, Principal Investigator(s): BA, Shen
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Exploring the usage and effectiveness of GAI in university students’ inquiry-based learning
This project addresses the intersection of artificial intelligence (AI) and education, with a particular focus on generative AI (GAI) and its role in facilitating inquiry-based learning among university students. With the rapid and irreversible development of various GAI products (e.g., ChatGPT), the conventional way of teaching and learning in higher education is facing challenges, including how students obtain information, generate ideas, and develop skills. On the one hand, GAI can contribute greatly to students’ learning by being available 24/7, answering any type of questions, and offering immediate feedback. On the other hand, inappropriate usage of or overly reliance on GAI can also hinder learning and lead to serious issues such as plagiarism and dishonesty. With such distinct advantages and disadvantages of GAI, it is important to identify how students use GAI during the learning process and determine patterns and strategies that are beneficial to learning while minimizing the negative effects. This project aims to employ a mix of quantitative, qualitative, and computational methods to explore how university students use GAI in inquiry-based learning activities and to assess the effectiveness of GAI in facilitating students’ various learning outcomes. The findings of this project will 1) reveal students’ typical types of GAI usages; 2) demonstrate good and bad practices of GAI and their impact on learning outcomes; and 3) inform the designs of guidelines and strategies guiding GAI-assisted inquiry-based learning.
Project Start Year: 2024, Principal Investigator(s): BA, Shen
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