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Engineering Education

created 2026-08-17 · updated 2026-09-30

Engineering Education — the study of how students learn engineering and how to teach it effectively, spanning how AI transforms engineering pedagogy, assessment, faculty development, and the engineering workforce. The engineering education articles in this knowledge base cluster around several themes: the figurative language instructors use to make sense of AI, ethical governance of AI use, embodied and multimodal assessment of conceptual understanding, and how AI is reshaping the engineering and computing workforce.

Questions to Consider

  • Engineering is a profession where graduates' decisions affect public safety and infrastructure. If students rely on AI to do design and Problem Solving work, what is at stake that wouldn't matter in a purely academic subject?
  • Engineering instructors in the same department often hold fundamentally different mental models of AI — some see it as a social 'agent,' others as a technical 'tool.' How might those divergent framings change what students learn to do with AI depending on which instructor they get?
  • Hands-on laboratory and embodied learning have long defined engineering education. If AI can simulate or even automate design tasks, does hands-on experience still matter — or is something genuinely lost when practice becomes simulated?
  • One line of research assesses engineering understanding by tracking students' gestures alongside their speech, finding that close gesture–speech coupling signals coherent conceptual understanding. What could a student's hands reveal about understanding that their words alone might hide?
  • Research finds ethical guidance about AI in engineering is mostly student-facing and compliance-oriented, with weak reciprocal accountability for faculty and institutions. Whose responsibility should the safe use of AI in engineering really be?

Introduction

Engineering education research is distinctive because it sits at the intersection of professional formation and rigorous STEM content. It emphasizes design, problem-solving, hands-on and laboratory learning, teamwork, and preparing graduates for professional practice. Design is also the ground it shares with Design Education, and the two divide it by what can be checked: engineering's assessed object admits calculable artifacts and carries public-safety accountability, while design education's is the studio process behind the artifact — sketch, iteration, critique — the very evidence generative tools can now produce without the process that once generated it. AI raises distinctive questions here: whether hands-on and embodied experience still matters when AI can simulate or automate design tasks; the professional and ethical stakes of AI use (engineers' decisions affect public safety, infrastructure, and Sustainability); and how AI reshapes the competencies graduates need and the workforce they enter.

How AI appears in the knowledge base's engineering education research

  • Faculty understanding and shared language: Gerhardt et al. analyze the metaphors engineering instructors use to describe AI, finding that most frame it either as a human-like "social being/agent" or as a "technical tool," and that instructors within the same department often hold fundamentally different mental models. Because metaphors both construct and constrain understanding, they argue a shared, accurate language is essential for Educational Development and departmental discussions about AI.

  • Ethics and responsible use: Osunbunmi et al. systematically review empirical studies of AI in undergraduate engineering education, identifying seven recurring forms of ethical guidance (transparency, accountability, student independence/agency, privacy, Academic Integrity, fairness/equity/bias, beneficence). They find ethical guidance is predominantly student-facing and compliance-oriented, with reciprocal faculty and institutional accountability underdeveloped — a concern heightened by engineering's direct stake in public safety and societal Well-Being.

  • Embodied and Multimodal AI assessment: Morphew et al. develop a multimodal framework integrating computer-vision gesture tracking with Large Language Models (LLMs) analysis of speech to assess engineering students' conceptual understanding of statistics, showing that gesture adds diagnostic evidence beyond speech and that close gesture–speech coupling signals coherent understanding.

  • Workforce transformation: Fletcher et al. review U.S. gray literature on AI and the engineering/computing workforce, framing the "Dual Train Problem" (rapid change vs. urgent policy) and recommending durable AI competencies, Ethics and AI Governance, and skill-based credentials for emerging roles.

  • Student adoption and reliance: Nguyen et al. extend the Technology Acceptance Model with critical use to model how engineering/CS students form intentions to use GenAI and how that intention predicts reliance across understanding, assessment, programming, and engineering-project tasks — finding moderate, appropriate reliance and heavy use for understanding-related tasks but limited use for full assessment writing. Asag & Al Mamun integrate TAM with UTAUT to model Bangladeshi engineering students' adoption, explaining 64% of usage variance and highlighting job relevance, result demonstrability, and subjective norms as key drivers.

  • Prompting strategy predicts performance, not usage volume: among 128 fourth-year engineering students, AI Query Efficiency and AI-Driven Problem-Solving were the strongest predictors of academic success and remained significant after controlling for cumulative GPA (Isaza Dominguez et al. (2026)).

  • Students' own metaphors exceed what the tools deliver: Kudina (2026) found 100 engineering students most valued LLMs for writing support, conceptual clarification and coding, yet framed them as "oracle" and "tutor" — authority and personalization a probabilistic text generator cannot supply, a "cruel optimism" that depends on verification skills students are still building.

  • Embed AI in the discipline, not beside it: a project-based thermal-engineering curriculum folded machine learning into existing thermal topics across introductory, application and advanced levels rather than adding computer-science courses, and undergraduates meeting only minimal entry requirements produced weaker projects than graduate peers (Li et al. (2026)).

  • AI tutoring and feedback for quantitative engineering courses: Yin et al. (2026) introduce Arthur, an AI teaching assistant that delivers real-time, personalized feedback on Calculated Formula Questions in an undergraduate Engineering Economics course — a domain where pen-and-paper, unstructured solutions have blocked prior AI support. Its full life-cycle pipeline (curating previously graded handwritten submissions, random-masking data augmentation, per-question XGBoost diagnosis backbones, and a dialogue-based question-bank web interface) offers a scalable pathway for AI feedback across engineering courses that lack structured digital data, and the framework is designed to generalize to CFQs in other engineering disciplines.

  • AI for sustainability, integrated rather than modular. The four-pillar AI-SEE framework (intelligence-driven, green-empowered, responsibility-leading, practice-integrated) distributes AI across engineering coursework; in a 144-student transportation-engineering case, students reported engagement across personal, academic, professional, and social levels, with sustainability reasoning carried into families and peer networks (Liu et al. (2026)).

  • A tool-rich studio can depress efficacy without improving the product. In a 27-student architectural design studio, teams using a generative-AI plus XR pipeline showed larger pre–post declines in design Self-Efficacy (β = −1.675) and outcome expectancy (β = −2.088) than controls, while one headset per student produced no coordinated collaboration (Xiao et al., 2026).

Signature concerns

Engineering education's signature concerns shape how AI is taken up: hands-on and laboratory learning (and what is lost when AI simulates practice), professional formation (ethical responsibility, safety, and societal impact), design and problem-solving (how much cognitive work AI should do), and competency-based preparation for the workforce. These make engineering education a rich site for studying whether AI augments or displaces the cognitive work essential to professional expertise — paralleling debates in Physics Education, Math Education, and CS Education.

Engineering education sits within STEM Education and connects strongly to CS Education (computing and software engineering), Math Education and Physics Education (engineering's mathematical and physical foundations), Workplace Learning and Educational Development (workforce and instructor preparation), Assessment and Ethics (the professional and evaluative stakes of AI), and Higher Education (the institutional context). It is the home discipline for the knowledge base's ASEE-sourced articles.

Under-covered sub-areas

The knowledge base's engineering education coverage is still developing. Sub-areas that would benefit from further articles include discipline-specific engineering pedagogies (mechanical, civil, chemical, electrical, software, and bioengineering education), design and maker education, capstone and project-based learning, and engineering ethics education — where AI's role is likely to be especially consequential.

Implications for engineering instructors

  • Build a shared, accurate language about AI. Faculty metaphor research finds instructors hold fundamentally different mental models (agent vs. tool); align departmental understanding before making policy.

  • Close the ethics-to-accountability gap. Ethics reviews find guidance is mostly student-facing and compliance-oriented; strengthen reciprocal faculty and institutional accountability, given engineering's stake in public safety.

  • Use multimodal, embodied assessment. Gesture + speech assessment adds diagnostic evidence beyond language alone — consider embodied cues when evaluating conceptual understanding.

  • Prepare students for the workforce, not just the course. The Dual Train Problem urges durable AI competencies, ethics/governance, and skill-based credentials aligned with emerging roles.

  • Model critical use and appropriate reliance. Critical-use TAM research shows students rely on AI heavily for understanding tasks but less for assessment — guide them toward appropriate, verifiable reliance across task types.

  • Teach AI use as a skill inside class time. Geng et al. (2026) ran nine instructor-led demonstrations, each 10–15 minutes at the end of a lecture and sequenced from tool literacy through strategic delegation to model evaluation — but students seeing no connection to assessment reported them redundant, so tie them to graded work.

Connected Concepts

Connected Articles

Metadata

DisciplineEngineering Education, Stem Education
LevelHigher Ed
Intended audienceLearners, Instructors, Faculty Developers
Learning and instructionWorkplace Learning
Assessment and measurementAssessment
Equity, ethics, and responsible useEthics
Foundations of AI in educationAI Literacy, Educational Development
SourceHistoryEditChat

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