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AI in Education

You may want to start by reviewing the self-paced Generative AI Modules on the AI basics, ethical implications, opportunities and risks, and responsible use.

(Developed by Carnegie Mellon University faculty, students and staff in 2025.)

 

AI Literacy and Instruction

 

The EDUCAUSE Working Group breaks down AI literacy into these areas:

  • Technical: Technical understanding of how AI works.
  • Evaluative: Critically evaluating AI tool applications and outputs, with an emphasis on developing robust AI impact assessment tools
  • Practical: Effectively applying, integrating, and managing AI tools across teaching, scholarship, and educational administration.
  • Ethical: Formulating and enforcing institutional strategies to safeguard against biases and the misuse or misapplication of AI technologies.

EDUCAUSE: Defining AI Literacy for Higher Education (for Faculty and Students)

 

Stanford University offers a framework and resources to help faculty navigate the opportunities and challenges of generative AI. 

 

 

Related: 

 

 

 

Opportunities

 

  • Personalized Learning: AI can adapt to individual student needs and learning paces
  • 24/7 Support & Instant Feedback: AI tutors can provide assistance outside class hours
  • Supporting Creativity: Assist with brainstorming and content development
  • Accessibility: Provide translation and alternative format support
  • Time Efficiency: Automate routine tasks such as generating quiz questions and providing initial grading

 

Kestin, Greg, et al. “AI Tutoring Outperforms In-Class Active Learning: An RCT Introducing a Novel Research-Based Design in an Authentic Educational Setting.” Scientific Reports, vol. 15, no. 1, Jun. 2025, p. 17458. DOI.org (Crossref), https://doi.org/10.1038/s41598-025-97652-6.

Owston, R. "Personalized AI Tutoring as a Social Activity: Paradox or Possibility?" EDUCAUSE Review, June 12, 2024. https://er.educause.edu/articles/2024/6/personalized-ai-tutoring-as-a-social-activity-paradox-or-possibility.

AI Tutor Pro by Contact North/Contact Nord 

AI Teaching Assistant Pro by Contact North/Contact Nord

 

 

Risks and Limitations

 

  • Academic Integrity: AI poses challenges in assessing authentic student work.
  • Bias and Discrimination: The Large Language Models may perpetuate biases in training data.
  • Cognitive Offloading: Students may over-rely on AI for academic tasks and not develop critical thinking skills.
  • Copyright Violation and Intellectual Property: AI models may be trained on copyrighted materials without permission. Additionally, course materials may be unintentionally uploaded, which AI may use to train models.
  • Environmental Impact: The growing number of data centers needed for AI model training and usage is driving a significant increase in energy and water demand.
  • Equity: Unequal access to AI tools can widen digital divide. Paid AI tools tend to provide more advanced reasoning and accurate responses than the free version.
  • Inaccuracy and Hallucination: AI-generated content can be completely false but confidently presented as fact.
  • Privacy Concerns: Data entered into AI systems may be stored, and used for training and forming outputs

 

AI Ethics (Elon University | Student Guide)

AI in Schools: Pros and Cons (University of Illinois Urbana-Champaign | College of Education) 

Ethical AI for Teaching and Learning (Cornell University | Center for Teaching Innovation) 

Kosmyna, N., et al. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv. https://arxiv.org/pdf/2506.08872.

O’Donnell, J. (2025, September 22). We did the math on AI’s energy footprint. Here’s the story you haven’t heard. MIT Technology Review. https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech.

 

 

 

  

 

Generative AI Tools

 

Randolph-Macon College community members have access to these Generative AI tools with enterprise data protection. [Why is this important? Refer to "Risks & Limitations".]

Other common Generative AI tools include ChatGPT (by OpenAI) and Claude (by Anthropic). Both provide free and paid versions. Additionally, many Apps offer limited free AI functions or a limited number of AI generations.

 

 

Crafting Effective Prompts

 

Quality AI output depends on clear, specific prompts. Key steps:

  • Decide and provide what the desired outcome is.
  • Provide context, such as
    • Who is the audience?
    • What is AI's role? 
    • Show the AI what you want through examples and sources
    • Set Constraints: Specify length, tone, and output format (text, table, image, etc.)
    • Have AI ask you questions for clarity.
  • Iterate: Refine prompts based on initial outputs.
  • Break complex tasks into smaller steps.

The CLEAR Framework of Prompt Crafting (Georgetown University)

Prompt Library by Lance Eaton (EDUCAUSE AI Resource)

Skill up on Prompts (Elon University Student Guide)

 

Related:

AI Hacks for Educators (University of Central Florida OER Works) is a comprehensive guide designed to help faculty leverage generative artificial intelligence (GenAI) tools, particularly Large Language Models (LLMs), to enhance their teaching, research, and professional development while lessening their workload."

Bowen, J. A., & Watson, C. E. (2024). Teaching with AI : a practical guide to a new era of human learning. Johns Hopkins University Press. 

 

 

 

 

Course AI Policies Considerations

 

Communicate expectations early and invite dialogue. 

  • Provide guidance on AI ethical use (referring to the Benefits, Risks and Tools sections of this website.)
  • Encourage students to share their thoughts, AI experience, concerns and expectations. Incorporate their feedback into the syllabus.

Be transparent about instructor use of AI. 

  • For example, if you use AI to generate quiz questions, let students know. Also communicate if AI generated content has been professionally verified for accuracy and relevance.

Define boundaries and permissions for student use.

  • When students may or may not use AI tools (e.g., AI tutoring, ideation for assignments, and final submissions).
  • Which tools are allowed, and which are not (e.g. code generators for programming assignment).
  • How to cite or annotate AI use (referring to the AI Literacy section on this website.)

   At the end of the semester, evaluate how the policy impacted student learning & academic integrity.

Encourage accountability & critical thinking. 

Ask students to document their AI use, for instance,

  • The AI tools & versions and prompts they used.
  • How AI responses were integrated into their thinking, workflow and the final products.
  • Steps taken to verify accuracy and relevance of AI-generated content.

Consider Accessibility and equity. 

  • Be mindful that not all students may have equal access to AI tools. Provide alternatives (referring to the "AI Tools" section on this website.)

 

Examples of AI Policy and User Expectations

 

Levels of student AI usage, such as allowed, partially allowed or prohibited:

Academic Policies on Generative AI: Collection from Universities

 

 
 

 

 

Research-based Principles of Learning

 

Key Learning Principles (Carnegie Mellon University Eberly Center)

  • Students’ prior knowledge can help or hinder learning.
  • How students organize knowledge influences how they learn and apply what they know.
  • Students’ motivation determines, directs, and sustains what they do to learn.
  • To develop mastery, students must acquire component skills, practice integrating them, and know when to apply what they have learned.  
  • Goal-directed practice coupled with targeted feedback enhances the quality of students’ learning.
  • Students’ current level of development interacts with the social, emotional, and intellectual climate of the course to impact learning.
  • To become self-directed learners, students must learn to monitor and adjust their approaches to learning.

Theory and Research-based Principle of Learning (Carnegie Mellon University | Eberly Center)

 

Teaching Principles

  • Understanding students' needs, background, motivation and prior knowledge, and using that information to inform course design and teaching
  • Align three student-focused components: learning objectives, assessments, and instructional activities for learning experiences
  • Continually assess and adapt to student needs and refine courses based on reflection and feedback

 

To align three student-focused components:

1. Identify Desired Results:

  • Articulate the Learning Objectives: What should students know or be able to do by the end of this module or course? 
  • Learning Objectives should include memorization and understanding, as well as higher knowledge levels such as evaluation and creation. See the Bloom's Taxonomy illustration below.

2. Measure if students have achieved the Learning Objectives.

  • The assessment should align with the learning objectives, and can be in the forms of quizzes, discussions, reflections and other ways which allow students to demonstrate their learning.

3. Plan learning experience and instruction. Select materials and design activities to help students reach the learning objectives and course goals. Prioritize the knowledge and skills over coverage 

  • Content - readings, videos and lecture notes
  • Student engagement - group work, hands-on activities and discussions
  • Scaffolding - breaking down complex content into manageable pieces, and organizing them to help students make connections; providing students with guided practice and feedback; and offering support
  • Promoting self-directed learning over time.

Teaching Principles (Carnegie Mellon University | Eberly Center)

Syllabus and Course Design(University of Illinois Chicago)

 

  

Student Motivation

 

Student motivation is a critical component of the learning process. It impacts how students engage with content, deal with challenges and take ownership of their learning. 

AI provides unique opportunities for personalized learning. Research findings have point to positive results of student interactions with well designed AI tutors. Refer to the Opportunities section of this page for details.

 

The ARCS framework helps foster and maintain student motivation through these phases:

  • Attention - Use perceptual surprise (e.g., unexpected examples), inquiry (thought-provoking questions), and presentation variety (videos, discussions) to capture and sustain interest.

  • Relevance - Connect content to the student's prior knowledge and goals; offer choices in access, sequence and topics based on student needs

  • Confidence - Communicate clear objectives and criteria; scaffold learning and support self-efficacy. 

  • Satisfaction - Provide timely and meaningful feedback, and opportunities for students to reflect on and apply learning.

 

The Universal Design for Learning principles enhance motivation by addressing diverse learner needs and preferences:

  • Provide Multiple Means of Engagement
    • Offer choices in how students engage, including varied topics, materials, or demonstration formats, to increase intrinsic interest.
    • Foster a sense of belonging by building inclusive class communities and validating diverse perspectives.  
  •  Support Sustained Effort
    • Normalize struggle, help students articulate goals, and emphasize how present tasks support future aspirations. 
  •  Multiple Means of Representation & Expression
    • Offer content in audio, visual, and textual formats, and allow students to demonstrate learning via various media (e.g. presentations, reflections, videos, essays), which bolsters motivation and self-efficacy (i.e. belief of one's own ability to achieve goals.)  

 

The ARCS Instructional Design Model of Motivation (Texas Tech University)

Boost Motivation with Universal Design for Learning (NC State University)

Universal Design for Learning (UDL): A framework​ for improving and optimizing teaching and learning for all people (Center for Applied Special Technology)

 

 
 

 

Active Learning

In the age of Generative AI, gathering information and even receiving explanations tailored to individual understanding has become increasingly available. Engaging students in active learning is more relevant than ever. Active learning encourages knowledge construction and skill development through participation, collaboration, and reflection. Consider these active learning practices:

  • Think-Pair-Share: Students reflect on a question individually, discuss their thoughts with a partner, and then share with the larger group. This promote reflection and peer learning.
  • Minute Papers: Quick written reflections at the end of class help students consolidate learning and give instructors feedback on understanding.
  • Concept Mapping: Students visually organize relationships between ideas, which supports knowledge understanding and integration.
  • Peer Instruction and “Jigsaw” Technique: Students work in groups where each member becomes an “expert” on one component of the topic. They then teach their peers, and help the group collectively build a comprehensive understanding.
  • Role Play or Simulations: Learners take on roles to explore perspectives and practice decision-making in complex scenarios.
  • Project-based Learning: Students engage in extended, authentic projects that require applying knowledge and skills to solve real-world problems or create meaningful products.

  Related: 

Active Learning (Cornell University Center for Teaching Innovation)

 

Assessment Redesign

Assessment should focus on student participation and reflective thinking. Here are some considerations:

  • Incorporate formative assessments: Frequent, low-stakes opportunities for feedback and improvement help monitor progress and discourage AI usage.
  • Prioritize the learning process over the final product: Design assessments that emphasize how students arrive at their conclusions (through drafts, reflections, and revisions) to reduce reliance on AI-generated answers.
  • Design authentic, personally relevant tasks: Require students to connect concepts to their own experiences or communicate ideas in real time (e.g., presentations and discussions).

 

 Assessment Ideas:

  • Frequent low-stakes timed quizzes: Use regular quizzes on fundamentals to reinforce mastery.
  • Platforms such as Google Docs with version history: Track drafts and edits to ensure students are involved in the writing process.
  • Personal application of theory: Assign tasks where students apply lecture concepts to their own experiences.
  • Scaffolded assignments: Break larger tasks into smaller deliverables with deadlines.
  • Discussion board participation: Evaluate ongoing engagement and thought development.
  • Peer review: Students critique each other’s work, reinforcing their understanding.
  • Reflective journals & process portfolios: Require documentation of learning processes, challenges, and reflections. This narrative is inherently personal.
  • Active and collaborative group work: Involve students in collectively setting group goals and individual accountability. 
  • In-person or recorded video presentations: Oral explanations ensure students can articulate and defend their own ideas.
  • Multi-modal storytelling: Have students express learning through formats like video, podcasts, infographics, or drawing. These formats require contextualized work.

  Related:

Course and Assignment (Re)designing Assessments and Specific Implications for Writing and Other Disciplines (University of Michigan)

Formative Assessment and Feedback (Stanford University)

Innovative Assessment Practices (Cornell University)

How to Write Effective Assignments (Harvard University) 

 

 
 

 

Teach Critical Thinking

 

Louis E. Newman's framework:

  • What is the claim? Is it supported by evidence?
  • Is the evidence credible and unbiased?
  • Are there alternative explanations for the topic?
  • What is the big picture? Why does the topic matter? Is it urgent? Why?

 

 
 

 

 

 

 

 

 

Books for Improving Teaching and Learning

AI in the Classroom - Books

Overviews of AI - Books

Selected List of Full-text eBooks