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

TL;DR

The paper investigates whether AI chatbots can coach engineering students toward mastery beyond information processing amid a disruption of traditional mentorship. Using a theoretical synthesis of Flores's intentionality and Goldberg's Five Shifts, it proposes a multiplex coaching framework that designates AI to convergent, technical tasks while reserving divergent, judgment-driven development for humans, within an AI-literacy-anchored governance model. A mixed-methods study (N=75 students, N=7 faculty) finds broad acceptance of AI for technical support but strong skepticism for its capacity to provide empathy, ethical guidance, or contextual judgment, with privacy emerging as a key governance requirement. The authors argue for hybrid, scalable coaching architectures that preserve human mentorship for core professional judgment and wisdom, while leveraging AI to democratize access to cognitive scaffolding and structured reflection.

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.

Figures (2)

• Figure 1: Perceived utility and risk of AI coachingamong engineering students and faculty. Error bars represent standard deviation (SD). While both groups find AI coaching similarly useful (no significant difference, $p=0.985$), faculty perceive markedly greater risks than students ($t=-3.98$, $p=0.003$, Cohen's $d=1.34$), revealing a clear trust boundary. **$p<0.01$.
• Figure 2: Views on Privacy and Data Governance:Faculty vs. Student. Frequency distribution of privacy preferences regarding AI coaching conversation logs (multi-select responses). The results confirm that both cohorts prioritize psychological safety and confidentiality, with 71.4% of faculty and 64.6% of students demanding that conversations "Must remain completely private and not accessible to instructors." This strong consensus serves as the primary justification for institutional data governance and accountability policies.