Harnessing AI to transform qualitative feedback analysis:insights from early-semester student surveys

Abstract

Format: Birds of a Feather Topic for discussion: Harnessing AI for student feedback analysis: what are the ethical and practical challenges? Context: Higher education institutions collect vast amounts of qualitative student feedback, yet traditional manual analysis is slow, resource-intensive, and often delays timely interventions (Flodén, 2016; Riger &amp; Sigurvinsdottir, 2016; Sozer et al., 2019). AI-powered tools offer a scalable and efficient alternative, but their use raises critical ethical and practical concerns. This session will explore these complexities, inviting participants to discuss both opportunities and challenges in implementing AI for qualitative feedback analysis. Description: This discussion draws from our experience implementing an AI-powered analysis tool at a research-intensive Australian university in the ASEAN region, where it processed over 20,000 qualitative responses from early-semester surveys. We will share insights into its practical application such as extracting themes, analyzing sentiment, and flagging critical alerts, while also highlighting the ethical and operational challenges that emerged. Intended outcome and contribution to scholarship/practice: This roundtable aims to foster critical dialogue on the integration of AI in qualitative feedback analysis, and, more broadly, in education. Participants will share their experiences, challenges, and best practices, contributing to a collective understanding of responsible and effective AI adoption in higher education. The session aims to identify key considerations for institutions exploring similar applications, promoting informed decision-making and ethical implementation. Engagement: Structured discussion prompts will guide the engagement: <br/>- How are your institutions currently using (or considering using) AI for qualitative feedback analysis? <br/>- What are the most pressing ethical concerns when using AI to analyze student feedback, and how can we mitigate potential risks? <br/>- How do we ensure that AI-driven analysis preserves the nuanced meaning and contextual richness of qualitative student feedback? <br/>- What are the next steps for institutions looking to implement AI in student evaluation processes?

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