Evaluating AI-powered learning assistants in engineering higher education with implications for student engagement, ethics, and policy

Key findings from this study

• The study found that nearly half of students reported greater ease seeking help from an AI assistant than from instructors or teaching assistants.
• The researchers identified ethical uncertainty regarding institutional policy and academic integrity as a key barrier preventing full engagement with the technology.
• The authors report that students regarded the AI assistant as supplementary to human instruction rather than a replacement for faculty guidance.

Overview

This study evaluates the Educational AI Hub, an AI-powered learning framework integrated into undergraduate civil and environmental engineering courses. The research examined student perceptions, engagement patterns, and concerns regarding trust, ethics, usability, and learning outcomes through mixed-methods analysis.

Methods and approach

The study employed a mixed-methods design across two undergraduate engineering courses at a large R1 public university. Researchers collected pre- and post-survey responses, system usage logs documenting over 600 AI interactions, and qualitative analyses of student-AI exchanges. Seventy-one students participated, generating approximately 100 survey responses alongside interaction data.

Results

Students valued the AI assistant primarily for accessibility and comfort, with nearly half reporting greater ease using it than consulting instructors or teaching assistants. The tool proved most useful for homework completion and concept clarification, though assessments of instructional quality remained mixed. Ethical uncertainty surrounding institutional policy and academic integrity standards emerged as a significant barrier to broader engagement.

Students conceptualized AI as supplementary to human instruction rather than a substitute. Usability, ethical transparency, and faculty guidance appeared critical to fostering meaningful engagement with the technology. System logs and survey data together revealed both quantitative adoption patterns and contextual barriers to fuller utilization.

Implications

The findings suggest that successful AI integration in engineering education requires explicit institutional policies addressing academic integrity and ethical usage. Faculty must provide clear guidance on appropriate AI applications within coursework to reduce uncertainty and maximize tool effectiveness. Transparency regarding AI capabilities and limitations remains essential for building student trust.

Developers and institutions should prioritize usability design and accessible documentation when deploying AI learning systems. The study indicates that students do not resist AI assistance per se, but rather seek clarity on acceptable integration practices. Institutions adopting similar frameworks should anticipate and address ethical concerns proactively rather than assuming technical quality alone drives engagement.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.