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Engineering Education

● high · created 2026-08-17 · updated 2026-08-21 · engineering-education, stem-education, ai-literacy, faculty-development, professional-training, assessment, ethics, higher-ed

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 wiki 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.

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. 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 wiki'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 Faculty 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 wellbeing.
  • Embodied and multimodal assessment: Morphew et al. develop a multimodal framework integrating computer-vision gesture tracking with LLM 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. grey 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 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.

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.

Connections to related concepts

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), Professional Training and Faculty Development (workforce and instructor preparation), Assessment and Ethics (the professional and evaluative stakes of AI), and Higher Ed (the institutional context). It is the home discipline for the wiki's ASEE-sourced articles.

Under-covered sub-areas

The wiki'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.

Connected Concepts

Connected Articles