AI Literacy for Academic Success

Canvas LMS | 6-Module Curriculum (2 Modules + Curriculum Architecture) | USU ITLS Graduate Project

The Problem

First-year college students face a contradiction: AI tools are reshaping the academic environment around them, but most students lack the foundational literacy to use those tools effectively or ethically. Some over-rely on generative AI in ways that undermine their own learning. Others avoid it entirely out of fear of plagiarism or simply not knowing where to start.  Neither outcome serves the learner or the institution. The need was clear: a structured, scaffolded curriculum that moves students from passive AI exposure to confident, ethical, field-ready AI application.

The Audience

Primary Learners:

First-year undergraduate students in higher education — including first-generation and non-traditional college students navigating both academic responsibilities and AI literacy simultaneously.

Learner Personas Identified Through Analysis:

• Students who use AI frequently but cannot evaluate or verify its outputs.

• Students who avoid AI entirely due to uncertainty about academic integrity.

• Students who recognize AI is relevant to their careers but lack formal training in how to apply it.

The needs analysis confirmed that this population's challenges were not primarily motivational — they were structural. Students needed a framework, not just encouragement.

The ADDIE Process

Analyze — Needs, Task, and Learner Analysis:

We began with a formal Needs Analysis to determine whether instruction was the appropriate intervention for AI literacy gaps — and to identify how institutions were currently responding to student's AI use. The analysis confirmed that most institutional responses were reactive (policy-based restrictions) rather than proactive (skill-building). Instruction was identified as the primary lever for sustainable change.

We then conducted a Task and Learner Analysis, including the development of learner personas, to understand the specific challenges, motivations, and academic pressures facing our target audience. This analysis directly shaped the module structure and scaffolding decisions.

Design:

Four scaffolded learning goals were identified:

1. Understand the Evolution and Mechanics of AI.

2. Master AI Tool Literacy and Prompt Engineering.

3. Navigating Ethics, Biases, and Academic Integrity.

4. Apply AI Learning to Discipline-Specific Integration.

These goals mapped directly to a six-module course structure, with each module building on the previous. The scaffolding design followed Bloom's Taxonomy of moving learners from remembering and understanding (Modules 1–2) through applying and analyzing (Modules 3–5) to evaluating and creating (Module 6).

Develop — Course Build in Canvas LMS:

Each module was standardized to one content page, one discussion post, and one major quiz or assignment — reducing redundancy and ensuring clean scaffolding between units. Content, quiz items, and assignment prompts were developed using a balance of symbolic, iconic, and enactive learning activities per Dale's Cone of Experience.

My primary responsibility was Modules 4 and 5: AI Limitations & Biases and AI Privacy & Ethics. These modules addressed the critical evaluation phase of the curriculum, giving students tools to identify hallucinations, recognize structural bias in AI outputs, and protect their own data.

Evaluate — Kirkpatrick's Four Levels

Evaluation was embedded throughout the course, not treated as a separate endpoint:

• Level 1 (Reaction): Discussion posts gauged learner engagement and initial responses to AI concepts.

• Level 2 (Learning): Module quizzes measured knowledge acquisition at each scaffolding level.

• Level 3 (Behavior): Applied assignments required students to demonstrate skills (fact-checking AI, anonymizing data, citing AI-assisted work) in context.

• Level 4 (Results): The final capstone required students to produce a field-specific professional solution integrating all prior skills.

Design Decisions 

Scaffold First, Assign Second: 

Early course iterations had 22–25 varied assignments with inconsistent difficulty progression. Redesigning around four thematic goals and standardized module formats reduced cognitive load and made the learning arc visible to students.

Generative AI as Both Subject and Tool:

We made a deliberate, documented decision to use generative AI in the design process itself — for brainstorming content, drafting quiz items, and generating scenario-based examples. This decision was discussed explicitly in the course as a model for transparent, ethical AI use.

Ethics and Privacy Modules: 

Rather than treating ethics as a single bullet point, we gave it dedicated instructional space (Modules 4 and 5). The reasoning: these are the highest-stakes competencies for adult learners entering AI-shaped workplaces, and they require more than exposure — they require practice.

Outcome

A published, six-module Canvas LMS course designed to take first-year undergraduate students from AI novice to competent, ethical AI practitioners. My contributions included co-designing the overall curriculum architecture and developing Modules 4 and 5 (AI Limitations & Biases and AI Privacy & Ethics) — the critical evaluation phase of the scaffolded sequence. The course demonstrates full ADDIE implementation, Bloom's Taxonomy scaffolding, and Kirkpatrick-aligned evaluation throughout.

Reflection

The most important insight from this project was that the scaffolding decision mattered more than any individual piece of content. The first version of this course was too broad and too fragmented. Changing to four clear goals and a consistent module format made the whole curriculum more coherent, additionally helping us to make real decisions about what was essential versus what was just interesting.

Working on the ethics and privacy modules specifically reinforced my belief that Instructional Designers have a responsibility to their learners that goes beyond content delivery. Students in this course weren't just learning about AI, they were developing the critical capacities that will shape how they engage with technology in their careers. That's the kind of design problem I want to keep working on.

University Foundations - ADDIE Document Overview

Read below for a full overview of the ADDIE Design Document details: