Can AI Chatbots Provide Coaching in Engineering? Beyond Information Processing Toward Mastery

2026-01-07

arXiv.org

AI Summary

This study addresses the decline of traditional apprenticeship models in engineering education and investigates the boundaries of AI’s role in fostering higher-order competencies such as judgment, tacit knowledge, and value-laden reasoning. Employing a mixed-methods approach with 75 students and 7 instructors, the research integrates generative AI chatbots, Likert-scale surveys, and in-depth interviews to propose a “multiplexed mentoring framework” that embeds human experts within AI-mediated tutoring loops—preserving the depth of apprenticeship while enhancing scalability. Findings indicate students acknowledge AI’s utility in technical problem-solving (mean = 3.84/5) but express widespread skepticism regarding its capacity for ethical, emotional, and contextual judgment. Notably, 64–71% of participants emphasized strict confidentiality, with instructors reporting significantly greater concerns about associated risks (p = 0.003). The study concludes that AI cannot substitute embodied, value-infused human mentorship.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Engineering education faces a double disruption: traditional apprenticeship models that cultivated judgment and tacit skill are eroding, just as generative AI emerges as an informal coaching partner. This convergence rekindles long-standing questions in the philosophy of AI and cognition about the limits of computation, the nature of embodied rationality, and the distinction between information processing and wisdom. Building on this rich intellectual tradition, this paper examines whether AI chatbots can provide coaching that fosters mastery rather than merely delivering information. We synthesize critical perspectives from decades of scholarship on expertise, tacit knowledge, and human-machine interaction, situating them within the context of contemporary AI-driven education. Empirically, we report findings from a mixed-methods study (N = 75 students, N = 7 faculty) exploring the use of a coaching chatbot in engineering education. Results reveal a consistent boundary: participants accept AI for technical problem solving (convergent tasks; M = 3.84 on a 1-5 Likert scale) but remain skeptical of its capacity for moral, emotional, and contextual judgment (divergent tasks). Faculty express stronger concerns over risk (M = 4.71 vs. M = 4.14, p = 0.003), and privacy emerges as a key requirement, with 64-71 percent of participants demanding strict confidentiality. Our findings suggest that while generative AI can democratize access to cognitive and procedural support, it cannot replicate the embodied, value-laden dimensions of human mentorship. We propose a multiplex coaching framework that integrates human wisdom within expert-in-the-loop models, preserving the depth of apprenticeship while leveraging AI scalability to enrich the next generation of engineering education.

Problem

• AI coaching
• engineering education
• tacit knowledge
• mastery
• human-AI interaction

Innovation

• multiplex coaching framework
• embodied rationality
• expert-in-the-loop
• generative AI in education
• tacit knowledge