It's Like "X": How Engineering Faculty Metaphors Construct (and Constrain) AI Understanding in Engineering Education

Synthesis: Gerhardt, Shiekh, Katz, and Chaback (2026) analyze the figurative language — metaphors and analogies — that engineering instructors use to describe generative AI, drawn from 57 semi-structured interviews across 17 disciplines and seven universities. They map instructors' language onto a five-dimensional taxonomy (Ontology, Epistemology, Operation, Relationship, Power/Capability), finding that instructors overwhelmingly frame GAI either as a human-like "social being/agent" (e.g., "a PhD student," "an assistant") or as a "technical object/artifact" (e.g., a search engine or tool), with almost no framing of GAI as an environmental or systemic force. Because these metaphors both construct and constrain understanding, and because instructors within the same department often hold fundamentally different mental models, the authors argue that developing a shared language is essential for Faculty Development and productive departmental discussions about GAI in engineering education.

Metaphor as understanding

The paper begins from the premise that figurative language is not decorative but fundamental to how we think and act: "the way we think, what we experience, and what we do every day is very much a matter of metaphor." Instructors inevitably describe GAI through metaphors — a "mirror" that reflects biases, a "crutch" or "drug" enabling Cognitive Offloading and intellectual degradation, an "assistant," or an "advanced student." Common metaphors for LLMs in wider discourse include "stochastic parrots," "simulators," "crowds/zeitgeist," "gods," "e-bikes," and "drop-in remote workers." In engineering specifically, figurative language is instrumental for conceptual understanding. Yet a lack of shared language between stakeholders can compromise Faculty Development programs and departmental discussions.

Five dimensions of instructor language

Analyzing instructors' responses to the prompt "if you had to think of an analogy or metaphor to describe GAI, what would you say?", the authors developed a five-continuum codebook, each with contrasting positions:

• Ontology — what "is" GAI? Instructors most often framed GAI as a Social Being/Agent (54.2%) with human-like agentic and relational qualities (e.g., "a person with deep knowledge who thinks far faster than I do"); second was a Technical Object/Artifact (44.1%) likened to a search engine or tool; least common was an Environmental Force/Systemic Phenomenon (1.7%) such as a tsunami, iceberg, or paradigm shift.
• Epistemology — does GAI genuinely understand? Positions ranged from "Genuine/True Understanding" to "Mimics/Simulates Understanding," often co-occurring with the human-like vs. mechanical framings above.
• Operation — how does it work? "Learning/Developmental" versus "Mechanical/Pattern-Matching."
• Relationship — how do we relate to it? "Collaborative/Partnership" versus "Instrument/Tool-Use."
• Power/Capability — what can it do? "Human-Level/Comparable" versus "Limited/Deficient."

Nearly 75% of "Social Being/Agent" framings co-occurred with Collaborative/Partnership, Human-Level/Comparable, Genuine/True Understanding, and Learning/Developmental positions — forming a coherent human-like-assistant mental model. Conversely, the "Technical Object/Artifact" framing co-occurred with mechanical, tool-use, and limited positions.

Constructing and constraining understanding

The key finding is that engineering instructors do not hold a unified mental model of GAI: even within the same department, semester, and institutional policy environment, instructors construct fundamentally different accounts of what GAI "is" and "means." None is inherently "correct," but each is shaped by personal experience, use cases, and professional background. Critically, some metaphors are inaccurate and can constrain understanding: describing GAI as a "search engine" inaccurately positions it as retrieving pre-existing information, when in fact it algorithmically generates plausible token continuations. Instructors tended to focus on what GAI does rather than what it knows, says, or is.

Implications for faculty development

The authors argue these metaphors function as "guidepoints" for self-reflection and Faculty Development: evaluating the range of language instructors use can help anticipate, assess, and intervene in how GAI operates in engineering education. A shared, accurate conceptual language supports developing training programs and facilitating adoption discussions that currently founder on incoherence between institutional and faculty language about GAI systems. For educators and developers, the work highlights that how we talk about AI shapes how students learn about, trust, and use it — connecting to AI Literacy and Student Experience.

Connected Concepts

• Engineering Education
• Faculty Development
• AI Literacy
• Generative AI
• Higher Ed
• Student Experience
• AI Misuse Learning Harm
• Cognitive Offloading
• AI Education

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Citation

Gerhardt, M., Shiekh, K., Katz, A., & Chaback, B. E. (2026). It's Like "X": How Engineering Faculty Metaphors Construct (and Constrain) AI Understanding in Engineering Education. ASEE Annual Conference & Exposition, Paper ID #50720.