Assessing students’ DRIVE: A framework to evaluate learning through interactions with generative AI

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As generative AI (GenAI) transforms how students learn and work, higher education must rethink its assessment strategies. This paper introduces a conceptual framework, DRIVE, and a taxonomy to help educators evaluate student learning based on their interactions with GenAI chatbots. Although existing research maps student-GenAI interactions to writing outcomes, practice-oriented tools for assessing evidence of domain-specific learning beyond general AI literacy skills or general writing skills remain underexplored. We propose that GenAI interactions can serve as a valid indicator of learning by revealing how students steer the interaction (Directive Reasoning Interaction) and articulate acquired knowledge into the dialogue with AI (Visible Expertise). We conducted a multi-methods analysis of GenAI interaction annotations (n = 1450) from graded essays (n = 70) in STEM writing-intensive courses. A strong positive correlation was found between the quality GenAI interactions and final essay scores, validating the feasibility of this assessment approach. Furthermore, our taxonomy revealed distinct GenAI interaction profiles: High essay scores were connected to a ”targeted improvement partnership” focused on text refinement, whereas high interaction scores were linked to a ”collaborative intellectual partnership” centered on idea development. In contrast, below-average scores were associated with ”basic information retrieval” or ”passive task delegation” profiles. These findings demonstrate how the assessment method (output vs. process focus) may shape students’ GenAI usage. Traditional assessment can reinforce text optimization, while process-focused evaluation may reward an exploratory partnership with AI. The DRIVE framework and the taxonomy offer educators and researchers a practical tool to design assessments that capture learning in AI-integrated classrooms.