Practical Applications of Artificial Intelligence

Photo of John Stakeley

John Stakeley, D.Sc. 
Business

Project Overview

I have put much thought into integrating technology into the classroom. I have made many changes over the past year. But due to the over whelming impact of Artificial Intelligence (AI), I have narrowed my Tech Fellows “project” on the practical implications and applications of AI.

Planning Process

In planning this project, my primary goal was to design a learning experience that prepares students to ethically and critically engage with AI as a professional tool, rather than attempting to prohibit or discourage its use. I approached the planning process as I would any major instructional design task: by aligning learning activities with course objectives, anticipating student needs, and intentionally embedding equity, accessibility, and academic integrity into the structure of the assignment. If we prohibit it’s use, the students will find ways around it. I developed an AI acceptable use policy, use AI in everyday class settings and have specific AI “assisted” assignments.

Implementation

Planning Considerations
Several key factors guided my planning:

1. The Ubiquity of AI Tools
   Students are already using AI—often informally, inconsistently, or without guidance. Ignoring this reality would increase inequities and potentially undermine learning. Instead, I designed the project to make AI use explicit, transparent, and accountable.

2. Ethical and Professional Preparation
   As a college professor, I see ethical AI use as part of students’ professional formation. Many of my students will enter fields where AI-assisted decision-making, writing, analysis, or synthesis will be routine. This project treats AI literacy as an ethical competency, not merely a technical skill.

3. Academic Integrity and Learning Depth
   The project was intentionally designed so that AI could not replace learning but could support it. Students were required to document, critique, and reflect on their AI use, shifting the emphasis from product to process.

4. Student Variability in Experience and Access
   I assumed wide variability in students’ familiarity with AI tools, confidence in writing, and access to technology. This assumption directly informed how I addressed Universal Design for Learning (UDL) and equity, described below.

Alignment with Course Learning Objectives and Teaching Goals
The project was explicitly aligned with both course learning objectives and my broader teaching goals.

Course Learning Objectives

This assignment directly supported objectives such as:

• Critical thinking and analysis: Students were required to evaluate AI-generated content rather than accept it uncritically.

• Ethical reasoning: Students examined ethical implications of AI use, including bias, accuracy, authorship, and responsibility.

• Communication clarity: Students revised and improved AI-assisted drafts to meet disciplinary standards.

• Metacognition: Through reflection, students analyzed how AI supported—or hindered—their learning.

Teaching Goals
From a pedagogical perspective, the project advanced my teaching goals by:

• Encouraging transparency and trust around AI use rather than surveillance or punishment.

• Modeling how professionals use tools responsibly rather than banning tools outright.

• Helping students develop a personal ethical framework for AI use they can carry beyond the course.

Rather than treating AI as a shortcut, the assignment framed it as a collaborative but imperfect assistant that requires human judgment.

Activity Design (Worksheet 2)
Using the Activity Design framework, the project was structured in stages:

1. Task Introduction and Norm Setting
   Students were provided with clear expectations about what types of AI use were permitted, required, or restricted. Examples of acceptable prompts and unacceptable uses were discussed explicitly.

2. Guided AI Engagement
   Students used AI for specific, bounded purposes (e.g., brainstorming, outlining, generating counterarguments), not full task completion.

3. Human Revision and Verification
   Students were required to fact-check, revise, and annotate AI-generated content, reinforcing that responsibility rests with the student—not the tool.

4. Reflective Component
   A metacognitive reflection required students to explain how they used AI, why they made certain decisions, and what ethical issues emerged.

This structure ensured that AI use enhanced learning rather than obscured it.

Models and Frameworks Used (Worksheet 3)

SAMR Model
I primarily used the SAMR model to guide integration:

• Substitution: AI replaced low-level tasks like initial brainstorming.

• Augmentation: AI provided feedback or alternative phrasings students could evaluate.

• Modification: The assignment itself was redesigned to include AI process documentation and critique.

• Redefinition: Students engaged in learning tasks (e.g., ethical analysis of AI collaboration) that would not be possible without the tool.

The project intentionally moved beyond substitution, ensuring AI use resulted in qualitative changes to learning, not just efficiency gains.

Bloom’s Taxonomy
Bloom’s Taxonomy also informed the project:

• Lower-order tasks (remembering, understanding) were supported by AI.

• Higher-order tasks—analyzing, evaluating, and creating—remained firmly the student’s responsibility.

• Reflection questions explicitly targeted evaluation and synthesis.

This prevented cognitive offloading and reinforced higher-order thinking.

Universal Design for Learning (UDL) and Equity
UDL Principles

The project was designed around all three UDL principles:

• Multiple means of engagement: Students could choose different AI tools or prompting strategies.

• Multiple means of representation: Instructional materials included written guides, examples, and in-class discussion.

• Multiple means of action and expression: Students demonstrated learning through both written work and reflective analysis.

Equity Considerations
Equity was addressed by:

• Avoiding requirements for paid AI tools.

• Teaching how to prompt effectively so prior technical knowledge did not become a hidden advantage.

• Valuing reflection and decision-making rather than polished output alone.

• Creating explicit space to discuss bias, hallucinations, and ethical risks—issues that disproportionately affect marginalized voices.

By making expectations explicit and process-oriented, the assignment reduced hidden curricula and leveled the playing field.

Assessment

I assessed this project using a combination of formal and informal assessment methods. This blended approach allowed me to evaluate both student learning outcomes, particularly in relation to ethical AI use.

• Formal assessment was used to evaluate written assignment.

• Informal assessment occurred throughout the project via class discussions, prompt-review activities, and brief student reflections on their AI use.

This is still a work in process.

Reflections and Next Steps

The students are using AI whether we like it or not. This is a critical adoption period where we can influence it’s ethic al use. I plan to refine my AI acceptance use policy as the technology matures. I will continue to include classroom use and AI assisted assignments in all my classes. This write up was assisted by Copilot.