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Introduction Grounded in self-regulated learning, we present a quasi-experimental design to examine how a generative artificial intelligence (AI) upgrade affects learning in an upper-level Environmental Data Science course. Methods We examine the educational effects of an AI upgrade (e.g., from ChatGPT-3.5 to ChatGPT-4o) by keeping the curriculum, pedagogy, and assessments constant across two cohorts (Spring 2024, n = 12 and Spring 2025, n = 13). Results The comparison suggests a significant gain in authentic, ill-structured project performance with no loss of AI-free conceptual and basic-skills exam performance, and points to a shift from peer-dependent to autonomous help seeking and increased positive sentiment toward AI support. Discussion Our central proposition is that effective pedagogical design is a necessary condition for AI upgrades to yield learning gains. Specifically, we advance the AI-affordance alignment framework where learning gains occur when AI capabilities are deliberately matched to task authenticity, the self-regulated learning phase, and pedagogical goals. The findings, while limited by small sample size, single-site design, and a focus on performance rather than direct measures of critical thinking, suggest that advances in AI can improve higher-order performance without eroding short-term conceptual recall, provided that effective pedagogical strategies for AI integration remain in place. These strategies include process transparency, scaffolded AI use, hybrid AI-resistant assessment checkpoints, metacognitive reflections, and active instructor mentoring. The study provides guidance for integrating current and future advances in AI into higher education, especially in research-based contexts where there is a need to balance innovation with the preservation of learner agency and academic integrity. Future research should directly investigate the impact of AI upgrades on student critical thinking and cognitive development.
Elshall et al. (Fri,) studied this question.