cs.CYMar 19, 2026
SaveFrom Understanding to Creation: A Prerequisite-Free AI Literacy Course with Technical Depth Across Majors
Organizations: Professor of Computer Science; Vice President and Chief AI Officer, George Mason University, Fairfax, Virginia, USA
Abstract
Most AI literacy courses for non-technical undergraduates emphasize conceptual breadth over technical depth. This paper describes UNIV 182, a prerequisite-free course at George Mason University that teaches undergraduates across majors to understand, use, evaluate, and build AI systems. The course is organized around five mechanisms: (1) a unifying conceptual pipeline (problem definition, data, model selection, evaluation, reflection) traversed repeatedly at increasing sophistication; (2) concurrent integration of ethical reasoning with the technical progression; (3) AI Studios, structured in-class work sessions with documentation protocols and real-time critique; (4) a cumulative assessment portfolio in which each assignment builds competencies required by the next, culminating in a co-authored field experiment on chatbot reasoning and a final project in which teams build AI-enabled artifacts and defend them before external evaluators; and (5) a custom AI agent providing structured reinforcement outside class. The paper situates this design within a comparative taxonomy of cross-major AI literacy courses and pedagogical traditions. Instructor-coded analysis of student artifacts at four assessment stages documents a progression from descriptive, intuition-based reasoning to technically grounded design with integrated safeguards, reaching the Create level of Bloom's revised taxonomy. To support adoption, the paper identifies which mechanisms are separable, which require institutional infrastructure, and how the design adapts to settings ranging from general AI literacy to discipline-embedded offerings. The course is offered as a documented resource, demonstrating that technical depth and broad accessibility can coexist when scaffolding supports both.
Figures & tables
Figure 1: Cross-major AI literacy courses positioned along two dimensions: audience breadth (horizontal axis) and the highest level of Bloom’s revised taxonomy [ 15 ] that students reach through the course’s own assessments (vertical axis). Color depth tracks Bloom’s level, with lighter greens at the Remember and Understand levels deepening to forest green at Create. Existing courses for non-majors cluster at the Understand and Apply levels. The literacy-competency-fluency taxonomy of [ 14 ] is shown for reference but does not span the full Bloom’s progression.
| Course / Framework | Institution | Prereqs | Bloom’s ceiling (non-majors) | AI engagement mode | Ethics model | Culminating assessment |
| Frameworks | ||||||
| Long & Magerko [ 1 ] | – | – | Unspecified | – | Included in competencies | – |
| AI4K12 [ 2 ] | – | – | Apply | – | Societal impact (big idea 5) | – |
| Tadimalla & Maher [ 3 ] | UNC Charlotte | None | Apply ∗ | Use tools; reflect | Parallel (Pillars 3–4) | AI literacy self-assessment |
| Courses | ||||||
| CS 309 [ 5 ] | UT Austin | None | Apply | Use sandboxes; solve non-coding problems | Parallel (videos + essays) | Individual written ethics project |
Table 1: Cross-major AI literacy courses and frameworks at the university level. Bloom’s ceiling indicates the highest level of Bloom’s revised taxonomy that non-major students are expected to reach through the course’s own assessments. AI engagement mode characterizes the dominant relationship students have with AI systems. Ethics model distinguishes whether ethical reasoning is delivered as a separate module (parallel), woven into technical content through a shared structure (integrated), or treated as the organizing principle of the course (ethics-centered). The final column identifies what the highest-stakes assessment asks students to produce or perform.
| Tradition | Core principle | Design problem addressed | Instantiation in UNIV 182 |
|---|---|---|---|
| Schema building through repeated traversal [ 24 ] | Robust understanding develops when learners encounter the same conceptual structure in progressively more complex contexts. | Mixed-background students lack a stable frame for organizing new technical material; each topic feels isolated and disposable. | A single conceptual pipeline (problem data approach train/configure metrics evaluate improve reflect) is traversed repeatedly, first with simple classifiers, then with neural architectures, then with ethically-consequential deployments. |
| Studio-based pedagogy (design education) | Concepts become disciplined action through structured work sessions with real-time critique, documented decisions, and peer accountability. | Non-majors can absorb concepts in lecture but fail to translate them into methodical practice; misconceptions remain latent until high-stakes assessments. | AI Studios at key junctures: building classifiers (no-code, then light code via Google Colab [ 30 ] ), preparing debates, designing experiments, developing prototypes. Protocols require written plans, evidence logs, and rationale before execution. |
| Assessment as learning [ 25 ] | Assessments designed as discovery tasks produce new understanding rather than measure existing knowledge. | Traditional content-verification exams reward recall and penalize the exploratory reasoning that non-majors need to develop; students with weaker technical backgrounds are disproportionately disadvantaged. | Cumulative portfolio in which each assessment feeds competencies forward: sector briefs supply evidence for debates; CNN ablations supply experimental discipline for the midterm; the midterm’s “fluency correctness” finding supplies evaluative habits for the final project. |
| Argumentation pedagogy; structured discourse | Holding opposing claims simultaneously, grounded in shared evidence, develops evaluative judgment that neither lectures nor reflection essays can produce alone. | Ethical discussion conducted without technical grounding reduces to opinion exchange; students form positions from headlines rather than from analysis. | “Two Things Can Be True” debates: teams defend opposing positions on AI deployment (facial recognition, AI art, healthcare, defense), with every argument required to follow the shared technical pipeline (problem data approach metric value judgment). |
Table 2: Pedagogical foundations informing the design of UNIV 182. Each row identifies a tradition from the learning sciences or professional education, the principle it contributes, the design problem it addresses in cross-major AI literacy instruction, and the course mechanism through which it is realized.
| Topics | Activities | Assessments |
| Foundations: The Pipeline, Data, and First Encounters | ||
| Introduction to AI; history from Aristotle to Hinton; AI in everyday life | Lectures; in-class discussion and debate comparing early aspirations with realities | – |
| How AI works: data, learning paradigms (supervised, unsupervised, reinforcement), evaluation | Lectures; data quality and bias case studies | Quiz (in-class) |
| When data and models go wrong | Studio : Hands-on with Teachable Machine; high-profile bias cases | HW1 assigned (AI ethics in a chosen domain) |
| Societal impacts of AI across domains (healthcare, education, climate, policy) | Flipped classroom: small-group discussion seeding HW1 and identifying debate topics | – |
| Structured Debates, Round 1: Responsible AI Innovation | ||
Table 3: Topic sequence, activities, and assessments for UNIV 182 (Fall 2025). The conceptual pipeline and ethical reasoning run concurrently throughout; what increases is the technical sophistication at which both are practiced. Fig. 2 visualizes this structure.
Figure 2: The structure of UNIV 182 as two continuous threads running beneath rising technical sophistication. The conceptual pipeline (orange ribbon) and ethical reasoning (green ribbon) run uninterrupted across the full semester. The blocks represent the technical content students engage with, rising in height to indicate increasing sophistication: from black-box models (Teachable Machine) through perceptrons and MLPs, CNNs and RNNs, transformers and LLMs, generative AI and agents, to the final build-and-defend project. Assessment markers are annotated below. Every assessment intersects both threads: no learning task in the course is purely technical or purely ethical.
| Assessment | Bloom’s Level + Scaffold | Feed-Forward Function |
|---|---|---|
| HW1: Sector brief (individual) | Un+An: Map case to pipeline; convert intuition to referenced claim | Evidence pool for debates; team recombination |
| Debates: “Two Things True” (team, live) | Ev: Studio-prepped argument via shared pipeline across high-stakes deployments | Normalizes complexity of real-world evaluation; reinforces evidence-based reasoning |
| HW2: CNN + saliency (individual, notebook) | Ap+An: Studio classifiers mechanistic accountability via controlled ablations, saliency interpretation | Experimental discipline (logs, controlled edits) transfers to midterm protocols |
| Midterm: Chatbot reasoning (team) | An+Ev: Design probes; controlled comparison; score correctness explanation separately | “Fluency correctness” finding; evaluative habits for HW3 and final safeguards |
| HW3: Create with AI (individual) | Ap Ev: Create artifact, log process, evaluate quality/bias/ownership/privacy/transparency | Ethics as experienced constraints; risk categories carry into final |
| Final: AI artifact + pitch (team, external) | Cr+Ev: Checkpoint studios timed pitch to external evaluators; defend problem, data, design, safeguards | Transfer test: method and accountability under scrutiny by evaluators external to the course |
Table 4: Assessment sequence (chronological). Each assessment builds competencies that feed forward into subsequent assignments. Bloom’s level: Un = Understand, Ap = Apply, An = Analyze, Ev = Evaluate, Cr = Create.
Figure 3: Assessment submission rates across the semester for all enrolled students (solid line, ) and course completers (dashed line, ). Assessments are ordered chronologically. For all enrolled students, submission rates held steady near 89% through the first three assessments, rose to 95% at the midterm, dipped to 79% at HW3 (an individual generative AI assignment in the final quarter), recovered to 100% for Debates Round 2 (an in-class team activity), and settled at 84% for the final project. The completers line shows that among students who finished the course, participation remained above 88% at every assessment point, with the same HW3 trough and Debates Round 2 recovery visible at a smaller magnitude.
| Item | Mean | Median | SD | Min |
|---|---|---|---|---|
| Environment facilitated engagement with content | 4.58 | 5.00 | 0.51 | 4 |
| Technologies/tools increased engagement | 4.58 | 5.00 | 0.51 | 4 |
| Encouraged expression of diverse perspectives | 4.58 | 5.00 | 0.67 | 3 |
| Learned through variety of opportunities | 4.45 | 5.00 | 0.69 | 3 |
| Gained understanding of main concepts | 4.42 | 4.00 | 0.51 | 4 |
Table 5: Selected items from institutional course evaluations, Fall 2025. All items scored on a 1–5 Likert scale. Items were selected for relevance to the course design claims; the full instrument contained 14 items.
Figure 4: Joint distribution of technical description mode and ethical reasoning mode across HW1 submissions. Each submission was coded along two dimensions: how the student described the AI technology (Descriptive, Pipeline-Mapped, or Mechanistic) and how the student reasoned about ethical consequences (General Concern, Pipeline-Referenced, or Causal Chain). The dashed border highlights the modal cell (42%): students who organized their technical analysis around the information management pipeline but whose ethical reasoning remained at the surface level, referencing the framework without tracing causal paths from design choices to downstream consequences. Only 10% of submissions contained causal chain reasoning at this stage.
Figure 5: Distribution of argument grounding levels across all distinct arguments coded from Debate Round 1. Arguments were classified as general societal concern (claims from intuition or media framing), pipeline-grounded (tracing at least two steps of the technical pipeline), or technically-specific (referencing model architecture, training methodology, or failure modes). Pipeline-grounded reasoning appeared in 39% of arguments, compared with 10% of HW1 submissions that contained causal chain reasoning. Technically-specific reasoning was essentially absent, consistent with the course design: the debate preceded the deep learning modules that would supply architectural knowledge.
Figure 6: Prevalence of three evaluative capabilities across HW3 submissions. Each submission was coded for the presence or absence of empirical testing (controlled variation or systematic probing of AI output), fluency-correctness distinction (separating surface quality from validity or originality), and provenance tracing (attempting to identify where output elements originated in training data or prior works). All three capabilities were essentially absent in HW1 submissions. Their emergence in HW3 is consistent with the scaffolding objective: the AI Studios built experimental discipline, the midterm supplied the fluency-correctness finding, and the cumulative portfolio extended into independent creative evaluation.
Figure 7: Prevalence and depth of four design integration competencies across final project team checkpoint documentation. Each team was coded for data pipeline skepticism (identifying specific bias sources in their own pipeline), causal ethics reasoning (tracing design choices to downstream consequences with evidence), failure mode design (designing around limitations rather than merely disclosing them), and constraint specification (defining what the system should not do). Bars show the proportion of teams exhibiting each competency with specificity (dark), at a surface level (medium), or not at all (light). The near-universal presence of these competencies, which were essentially absent in HW1, is consistent with the claim that the scaffolding mechanisms accumulated across the semester.
| Assessment | Stage-specific code | Bloom’s level | Justification (cognitive process) |
|---|---|---|---|
| HW1 | Descriptive (technical) or General Concern (ethical) | Remember / Understand | Recall or surface-level characterization of AI systems; no decomposition or application of a framework |
| Pipeline-Mapped (technical) + Pipeline-Referenced (ethical) | Apply | Use of a provided framework (the information management pipeline) to organize analysis, without independent decomposition | |
| Mechanistic (technical) or Causal Chain (ethical) | Analyze | Tracing relationships between technical components and downstream consequences; differentiating causes from effects | |
| Debate Round 1 | General societal concern | Apply | Constructing claims from intuition or media framing; assembling an argument without decomposing it through the pipeline |
| Pipeline-grounded | Analyze | Tracing at least two steps of the technical pipeline to derive a conclusion; differentiating data, model, and outcome | |
| Technically-specific | Evaluate | Referencing architecture or training methodology to judge system behavior against an independent technical criterion |
Table 6: Mapping of assessment-specific codes to Bloom’s revised taxonomy levels. Each row shows the assessment stage, the code assigned during stage-specific analysis, the Bloom’s level to which the code was mapped, and the cognitive process that justifies the mapping. Cognitive process definitions follow from work in [ 38 ] : Remember/Understand involves retrieving or constructing meaning from information; Apply involves carrying out a procedure in a given situation; Analyze involves breaking material into parts and determining how parts relate to one another; Evaluate involves making judgments based on criteria and standards; Create involves putting elements together to form a novel, coherent whole.
Figure 8: Distribution of coded reasoning across Bloom’s revised taxonomy levels at four assessment stages. At each stage, the proportion of reasoning at each taxonomic level was estimated from the stage-specific codings reported in the preceding subsections. In HW1, reasoning was concentrated at Remember/Understand and Apply (90% combined). By the final project, 95% of coded reasoning operated at the Analyze, Evaluate, or Create levels, with Create appearing at substantial scale (40%) for the first time. The upward trajectory of the distribution across the semester is consistent with the claim that the course’s scaffolding mechanisms produced cumulative growth in higher-order competencies among non-major undergraduates.
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