Thinking With AI: Human–AI Interaction and Critical Thinking in Scenario-Based Learning

Abstract: The rapid adoption of generative artificial intelligence (GenAI) in higher education raises important questions about how human–computer interaction (HCI) shapes students’ reasoning and critical engagement. This study examines how guided classroom interaction with AI, designed to support comparison and reflection, influences undergraduate students’ critical thinking and awareness of artificial intelligence (AI)-related biases. The activity was conducted at the Tecnologico de Monterrey, Mexico, in late 2025 through a face-to-face classroom exercise focused on space exploration and future geopolitical scenarios. Students first developed arguments and scenarios collaboratively without AI; AI tools were then introduced to enable structured comparison and critique of AI-generated content in terms of depth, creativity, stakeholder inclusion, and bias, positioning AI as a comparative artifact rather than a decision-making agent. The intervention was implemented in two undergraduate courses (Global Public Goods and Geopolitics and Technology) using identical interaction design. A mixed-methods pre/post design was applied. Quantitative data from six Likert-scale items aligned with the CAE Critical Thinking framework were analyzed using Wilcoxon signed-rank tests in Minitab (paired samples: n = 15 and n = 18), revealing statistically significant improvements across all dimensions (p < .01). Qualitative findings indicate increased awareness of AI limitations and biases. Students consistently perceived human-generated scenarios as more creative, while AI outputs provided more data but tended toward superficial, mainstream framing. Together, the findings underscore the value of guided, HCI-informed classroom design in leveraging GenAI to enhance—rather than replace—critical thinking.

Keywords: Human–AI interaction in higher education, Critical thinking development, Scenario-based learning, Educational innovation, Human–data interaction and AI bias

DOI: 10.54941/ahfe1007166

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