Designing AI-Era Learning Experiences

Designing AI-Era Learning
with Mayer’s Principles, UDL, and High-Impact Practices

Photo by Kelly Sikkema on Unsplash

Introduction

This guide offers a practical blueprint for building online learning that is evidence-based, inclusive, and future-ready. It blends three powerful frameworks: Mayer’s multimedia principles for cognitive effectiveness, Universal Design for Learning (UDL) for equity and accessibility, and High-Impact Practices (HIPs) for deep, applied engagement. Together, they help teams create courses that are clear, motivating, and built for durable learning and transfer while thoughtfully leveraging AI where it adds value.

Theoretical Framework

Mayer’s (2001) multimedia principles show how words and images can be orchestrated to minimize extraneous load and maximize meaningful learning. Applied well, they move courses beyond presentation into active processing, retention, and transfer.

Universal Design for Learning (UDL) ensures courses work for all learners by proactively embedding multiple means of representation, engagement, and expression. UDL both reduces barriers and expands learner choice and agency (CAST, 2018).

High-Impact Practices (HIPs) emphasize authentic, collaborative, feedback-rich learning that builds persistence, belonging, and real-world readiness. Designing with HIPs keeps online experiences rigorous, reflective, and transformative (AAC&U, 2025).

Updating Multimedia Principles for the AI Era

Mayer’s foundations remain essential. What’s new is the chance to personalize and scaffold without losing clarity: AI can help declutter materials, offer alternative modes, provide formative nudges, and support multilingual access. This expansion dovetails with UDL’s emphasis on flexibility and equity and with HIPs’ emphasis on active, reflective, and applied learning. Equally important are guardrails: transparency about when/how AI is used; privacy and academic-integrity norms; and learner agency in choosing when to use AI support.

The 12 Revised Principles (with AI-era tactics)

For each principle, keep the classic core, then consider design-time and learner-side enhancements. Use AI as a progressive enhancement, not a requirement.

1. Coherence → Dynamic Coherence
   Classic: Exclude unnecessary words, pictures, sounds.
   Design with AI: Auto-summarize/declutter; provide “concise vs. extended” versions.
   Learner use: Let learners ask a GPT to condense, define terms, or expand examples without changing core content.
   
   
2. Signaling → Adaptive Signaling
   Classic: Use cues to highlight essentials.
   Design with AI: Insert data-triggered cues (“pause & try,” icons) based on pain points or analytics.
   Learner use: GPT tutors nudge reflection or practice when confusion is detected.
   
   
3. Redundancy → Learner-Controlled Redundancy
   Classic: Prefer narration + graphics over narration + graphics + on-screen text.
   Design with AI: Offer captions/transcripts/summaries as optional layers (auto-generated, human-checked).
   Learner use: Toggle captions/transcripts; request multilingual summaries on demand.
   
   
4. Spatial Contiguity → Extended Spatial Contiguity
   Classic: Place related words and pictures near each other.
   Design with AI: Auto-align labels/callouts near visuals; generate region-specific alt text.
   Learner use: Context-aware chat sits next to the exact diagram/frame.
   
   
5. Temporal Contiguity → Synchronized Temporal Contiguity
   Classic: Present corresponding words and visuals together.
   Design with AI: Sync narration, captions, highlights; auto-chapter long videos.
   Learner use: “Explain what happened at 03:14” prompts time-linked explanations.
   
   
6. Segmenting → Segmenting with AI Scaffolding
   Classic: Present learner-paced segments.
   Design with AI: Break into micro-units with adaptive reflection/branching and pause nudges.
   Learner use: GPT helps pacing, micro-goals, and targeted review plans.
   
   
7. Pre-training → AI-Supported Pre-training
   Classic: Provide key concepts up front.
   Design with AI: Auto-glossaries, prerequisite checks, and role-based primers.
   Learner use: Ask GPT for analogies or tailored pre-reads before heavier content.
   
   
8. Modality → Expanded Modality
   Classic: Words + pictures > words alone; favor audio narration with visuals over dense text.
   Design with AI: Generate alternative modes (podcast recap, infographic, interactive walkthrough) from one source.
   Learner use: Choose preferred mode (audio recap, checklist, sim).
   
   
9. Multimedia → Generative Multimedia
   Classic: Present words + pictures (with purpose).
   Design with AI: Generate purposeful diagrams/storyboards/data views (with clear learning intent).
   Learner use: Co-create images/charts with GPT, then critique/revise for accuracy.
   
   
10. Personalization → Relational Personalization
    Classic: Use conversational, learner-friendly style.
    Design with AI: Tune tone for inclusivity; adapt examples to roles/contexts while keeping essentials constant.
    Learner use: GPT/avatars tailor explanations to goals, building confidence and persistence.
    
    
11. Voice → Authentic Voice Principle
    Classic: Human voice generally outperforms synthetic for narration.
    Design with AI: Use expressive TTS/avatars for access needs; keep a consistent narrator; disclose AI-generated audio.
    Learner use: Offer voice options (pace/accent) with content parity.
    
    
12. Image → Presence with Purpose
    Classic: Speaker image isn’t inherently helpful.
    Design with AI: Use instructor video/agents only to model thinking, demonstrate processes, or give feedback.
    Learner use: Avatars simulate scenarios for safe practice with targeted feedback.

Cross-cutting practices

• Transparency & Ethics: Clearly label AI-generated/adapted assets; publish privacy and integrity norms.
  
  
• Agency & Co-Creation: Invite AI-supported brainstorming/drafting with critique, revision, and attribution norms.
  
  
• Data-Informed Feedback: Provide growth-oriented dashboards and formative nudges to guide next steps.
  
  
• Generative Collaboration: Make authorship expectations explicit when AI assists group writing or media creation.

Alignment Matrix (Mayer + UDL + HIPs → Practice)

Revised Principle	UDL Alignment	HIPs Alignment	From Principle to Practice
Dynamic Coherence	Reduces barriers via clear, adjustable content	Scaffolds authentic tasks with clarity	Design: Auto-summaries; concise/extended views. Learner: GPT to condense/expand terms.
Adaptive Signaling	Supports self-regulation & attention	Formative checks; reflective practice	Design: Data-triggered cues. Learner: GPT prompts to pause/try/reflect.
Learner-Controlled Redundancy	Multiple means of perception & expression	Agency in revision processes	Design: Optional captions/transcripts/summaries. Learner: Toggle/multilingual.
Extended Spatial Contiguity	Clarifies perception; reduces split attention	Analysis of real artifacts	Design: Co-located labels; alt text per region. Learner: Chat anchored to artifact.
Synchronized Temporal Contiguity	Timing supports comprehension	Demonstration aligned to practice	Design: AI-synced narration/captions/chapters. Learner: Time-linked explanations.
Segmenting w/ AI Scaffolding	Manages load; supports metacognition	Structured practice with feedback	Design: Micro-units + adaptive prompts. Learner: Plan pacing/review with GPT.
AI-Supported Pre-training	Builds background knowledge	Readiness for integrative tasks	Design: Auto-glossaries, primers. Learner: Analogies/primers on demand.
Expanded Modality	Multiple means of representation & engagement	Authentic, inquiry-based work	Design: Alternate modes (audio/infographic/sim). Learner: Choose preferred path.
Generative Multimedia	Purposeful visuals reduce extraneous load	Creation with critique	Design: Generate diagrams with clear purpose. Learner: Co-create and critique.
Relational Personalization	Belonging; responsive language	Mentoring; instructor presence	Design: Inclusive, role-tuned examples. Learner: Goal-aware explanations.
Authentic Voice	Transparent options; autonomy	Authentic dialogue & artifacts	Design: Consistent narrator; disclose AI voice. Learner: Pace/accent choice.
Presence with Purpose	Representation that supports engagement	Guided practice; modeling	Design: Video/avatars only when they add value. Learner: Avatar sims + feedback.
Transparency & Ethics*	Equitable, transparent design	Trust for collaboration	Design: Label AI; publish guardrails. Learner: Reflect and attribute AI use.
Agency & Co-Creation*	Multiple means of expression	Applied, participatory learning	Design: AI-supported creation tasks. Learner: Draft→critique→revise cycle.
Data-Informed Feedback*	Executive function & self-monitoring	Iteration, reflection (e.g., ePortfolios)	Design: Growth dashboards, nudges. Learner: Monitor progress, set next steps.

*Cross-cutting practices applied alongside all 12 principles.

Putting it to work: Now / Near / Next Roadmap

Use this phased roadmap to turn principles into practice without waiting on perfect tools. Start with Now (no-AI essentials that strengthen clarity, signaling, segmentation, and reflection) moves that are platform-agnostic and lift quality for every learner. When ready, layer Near enhancements that use lightweight AI (auto-summaries, multilingual captions, time-coded chapters, simple nudges) to expand access and feedback without re-architecting your course. In Next, scale personalization and practice with context-aware help at the point of need, adaptive pathways, avatar simulations, and learner-facing growth dashboards. Treat the phases as additive and mix-and-match: choose feasible items, pilot with clear success criteria, and document guardrails for privacy and integrity. The aim is steady, evidence-based improvement that honors Mayer’s principles, advances UDL, and embeds HIPs in everyday learning.

• Now (no-AI baseline): Tighten coherence; add explicit signals; provide optional captions/transcripts; break lessons into micro-segments with planned pause prompts; use checklists and worked examples; add reflection before/after practice.
  
  
• Near (light AI support): Auto-summaries; multilingual captions; time-linked video chapters; simple analytics-based nudges; role-tuned examples.
  
  
• Next (scaled AI & data): Context-aware help next to artifacts; adaptive practice pathways; avatar simulations with targeted feedback; growth dashboards with learner-controlled data views.

You don’t need cutting-edge tools to honor these principles. Start with clarity, accessibility, and authentic practice. When AI is available, use it to enhance, not replace, good design. The result is online learning that is cognitively sound (Mayer), universally accessible (UDL), and deeply engaging (HIPs) built for real learners in real contexts.

Course Design Checklist

The following is a sample comprehensive, modality-agnostic, checklist you can use for in-person, hybrid, or online learning. It aligns Mayer + UDL + HIPs and adds on-ground considerations (room setup, live captioning, materials) alongside digital ones. Use “(if applicable)” to skip items that don’t fit your context.

A) Foundations & Alignment (any modality)

• Outcomes are measurable and mapped to activities, media, and assessments.
• Each session/module opens with a brief overview + relevance hook (why it matters).
• Worked examples, concept maps, or demonstrations clarify complex ideas.
• Reflection points appear before and after practice (think-alouds, exit tickets, discussion).
• Workload expectations (time-on-task) are clear for all formats.
• Logistics are set: space/tech checked, sightlines/audio verified, backup plan ready (slides/handouts/QRs).

B) Mayer’s 12 Principles with AI-Era Tactics (Now / Near / Next)

1) Coherence → Dynamic Coherence

·        Now: [ ] Trim extraneous text/images; provide concise + extended versions (handout/slide notes).

·        Near: [ ] Auto-summaries for slides/handouts (human-checked).

·        Next: [ ] Personalization rules surface concise vs. extended per learner profile.

2) Signaling → Adaptive Signaling

·        Now: [ ] Headings, callouts, and verbal cues; clearly marked “pause & try” moments.

·        Near: [ ] Time-coded chapters in recordings; simple analytics/polling nudges.

·        Next: [ ] Data-triggered cues adapt based on performance/engagement.

3) Redundancy → Learner-Controlled Redundancy

·        Now: [ ] Don’t read slides verbatim; pair narration + visuals; provide notes separately.

·        Near: [ ] Optional transcripts/captions/summaries; printed/large-print alternatives.

·        Next: [ ] Learners set default display (captions, transcript pane, summary language).

4) Spatial Contiguity → Extended Spatial Contiguity

·        Now: [ ] Labels/callouts next to visuals (slides, whiteboards, handouts, lab setups).

·        Near: [ ] Region-specific alt text/annotations; doc-cam views aligned to diagrams.

·        Next: [ ] Context-aware help/chat anchored to the exact artifact or station.

5) Temporal Contiguity → Synchronized Temporal Contiguity

·        Now: [ ] Align narration with demonstrations or board work; avoid lag.

·        Near: [ ] Auto-chapter recordings; verify caption timing; clickable agenda.

·        Next: [ ] “Explain this moment” prompts return time-linked micro-explanations.

6) Segmenting → Segmenting with AI Scaffolding

·        Now: [ ] Chunk sessions into micro-segments with movement or pause prompts.

·        Near: [ ] Adaptive reflection checks at boundaries (clickers, polls, quick writes).

·        Next: [ ] Branching pathways recommend revisit/advance per learner data.

7) Pre-training → AI-Supported Pre-training

·        Now: [ ] Key terms/priors up front; quick diagnostic or warm-up.

·        Near: [ ] Auto-generated glossaries/role-based primers (reviewed).

·        Next: [ ] Dynamic primers adjust to background-knowledge signals.

8) Modality → Expanded Modality

·        Now: [ ] Pair words + pictures; use physical models/manipulatives where relevant.

·        Near: [ ] Alternate modes (audio recap, infographic, lab demo video, checklist).

·        Next: [ ] Learners choose preferred mode; system remembers preferences.

9) Multimedia → Generative Multimedia

·        Now: [ ] Each visual/prop/demo has a clear learning purpose tied to outcomes.

·        Near: [ ] Generate supportive diagrams/storyboards; instructor verifies fidelity.

·        Next: [ ] Learners co-create visuals and critique for accuracy (studio time, gallery walk).

10) Personalization → Relational Personalization

·        Now: [ ] Conversational, inclusive tone; examples reflect diverse contexts.

·        Near: [ ] Role-tuned examples by learner choice (track cards, stations).

·        Next: [ ] Goal-aware tutoring/agents tailor explanations and practice sets.

11) Voice → Authentic Voice Principle

·        Now: [ ] Consistent voice; mic use for audibility; avoid talking while facing away.

·        Near: [ ] Offer pace/accent options in recordings; disclose synthetic narration.

·        Next: [ ] Selectable voice options with content parity.

12) Image → Presence with Purpose

·        Now: [ ] Instructor presence (live or video) used to model thinking/process.

·        Near: [ ] Short demo clips; doc-cam for procedures; targeted feedback videos.

·        Next: [ ] Avatar simulations for role-play with standards-aligned feedback.

C) Universal Design for Learning (UDL)

Multiple Means of Representation

• Captions/CART or interpreters for live sessions (if applicable); transcripts for recordings.
• Key ideas available in more than one format (slide + handout; diagram + description; model + photo).
• Reading order, headings, and materials are screen-reader friendly; print alternatives available.

Multiple Means of Engagement

• Choice in topics/examples; relevance to authentic contexts.
• Predictable structure and visible progress cues (agenda, timers, checkpoints).
• Low-stakes practice with immediate feedback (clickers, whiteboards, short quizzes).

Multiple Means of Action & Expression

• Options to demonstrate learning (paper/prototype/presentation/video/diagram).
• Rubrics describe quality across modalities (clarity, evidence, accuracy, reasoning).
• Supports for planning/executive function (checklists, timelines, milestones).

D) High-Impact Practices (HIPs)

• Authentic tasks mirror real problems, audiences, data, or clients.
• Frequent, substantive feedback (instructor, peer, automated formative).
• Significant time-on-task with staged milestones and revision cycles.
• Structured collaboration (roles, norms, equitable contribution checks).
• Diverse/global perspectives integrated into cases/examples.
• Threaded reflection (pre/during/post; learning journals; exit tickets).
• Culminating product (showcase, ePortfolio, poster session, demo day).

E) Assessment, Integrity, & Transparency

• Outcomes ↔ activities ↔ assessments are explicitly aligned.
• Rubrics cover content knowledge and process/communication.
• Clear AI use policy (allowed/restricted/prohibited) with examples.
• Citation/attribution norms for AI-assisted work; process evidence when needed.
• Integrity strategies: unique prompts, oral checks, versioned drafts, studio critiques.

F) Accessibility, Safety, & Logistics (Digital + Physical)

• Room & tech check: sightlines, audio, microphones, assistive listening systems.
• Lighting/contrast adequate; do not rely on color alone to convey meaning.
• Alt text/long descriptions for images/figures; tactile/large-print options (if applicable).
• Media players support captions, transcripts, and playback speed.
• Wayfinding & seating: accessible routes, reserved seating as needed.
• Downloadable or printed equivalents for interactive activities when feasible.
• Safety protocols for labs/fieldwork clearly briefed and posted.

G) Data-Informed Feedback & Analytics

• Formative checks inform next steps (reteach, extension, office hours).
• Learners can view progress and set goals (“next best action”).
• Instructor has a light-touch triage view (attendance, polls, quiz items).
• Data used to improve design, not surveil; privacy noted.

H) Ethics, Privacy, & Disclosure (AI & Recording)

• Label any AI-generated/adapted media; human review for accuracy/fairness.
• Plain-language privacy/recording notice; opt-out alternatives when feasible.
• Avoid uploading personally identifiable or protected data in tools.
• Provide non-AI paths for essential learning if AI is restricted.

I) Now / Near / Next Plan (per course, unit, or workshop)

• Now (no-AI baseline) items implemented (coherence, signaling, segmentation, reflection).
• Near pilots chosen with success criteria, small cohort, and timeline.
• Next roadmap drafted with dependencies (policy, platform, budget).
• Owner + due date tracked for each item.

J) Pre-Launch & Continuous Improvement

• Peer review against this checklist; issues resolved.
• Usability check with a few learners on flow and clarity (in person or remote).
• Accessibility spot-check (screen reader + keyboard only; live CART test if used).
• Pulse survey includes items on clarity, cognitive load, relevance, and access.
• Regular review cycle (e.g., each term) to refresh materials and data rules.

References:

Association of American Colleges & Universities (AAC&U). (2025). High-impact practices. https://www.aacu.org/trending-topics/high-impact-practices

CAST. (2018). Universal Design for Learning guidelines version 2.2. http://udlguidelines.cast.org

Mayer, R. E. (2001). Multimedia learning. Cambridge University Press.

National Standards for Quality Online Learning (NSQOL). (2025). National standards for quality online courses. https://nsqol.org/national-standards-for-quality-online-learning/

Embracing AI in Higher Education | Overcoming Fear and Unlocking Innovation

 

Embracing AI in Higher Education

Overcoming Fear and Unlocking Innovation

Photo by Possessed Photography on Unsplash

Background

The prospect of integrating AI into teaching, learning, or administrative work often triggers anxiety, skepticism, or resistance. Drawing on insights from the psychology of change, this article invites faculty and staff to see AI not as a threat, but as an opportunity to enhance their practice, deepen student engagement, and build new pathways for success.

Research shows that resistance to change is a natural human response rooted not in an aversion to innovation, but in fear of losing the familiar (Bridges, 2009). In higher education, these concerns often include fear of obsolescence, ethical worries, overwhelm with complexity, and identity threats related to one’s role as an educator or administrator.

For instance, studies suggest that faculty commonly worry that AI might deskill teaching or reduce human connection with students (Kim & Kim, 2022). Others fear that algorithmic bias could harm students or widen equity gaps (Baker & Smith, 2019). These fears are legitimate but they are also surmountable.

A particularly acute worry among staff and faculty in higher education is that AI will make many administrative roles redundant. This fear is understandable as AI tools can automate tasks like scheduling, form processing, financial forecasting, and even admissions support. Studies have found that administrative professionals feel vulnerable to job loss when tasks they perform are framed as easily automated (Bessen et al., 2019).

However, the reality is more nuanced. Most administrative tasks involve not just repetitive actions but also judgment, context-awareness, and relationship-building, which are all areas where humans remain essential. Rather than wholesale replacement, AI offers a path to augmentation by helping staff do their work more efficiently and effectively while focusing on higher-value, human-centered tasks.

Standing on the Shoulders of Our Greatest Tools

Throughout history, transformative tools have reshaped what we do and how we do it by freeing our minds and time for higher-level thinking and creativity. The fear many feel today about artificial intelligence echoes anxieties that once surrounded these now-indispensable inventions. Just as people once worried that electric light would disrupt natural rhythms, that calculators would erode mathematical skills, or that computers would replace human jobs, today’s concerns about AI reflect a familiar pattern of initial apprehension before widespread acceptance and progress

In fact, the electric light liberated human productivity from dependence on daylight. Candles and oil lamps were dangerous and dim. Light bulbs expanded when and where we could read, study, and collaborate which unlocked a new era of economic and intellectual growth. Similarly, before calculators, performing long division or complex calculations was time-consuming and error-prone, limiting the scope of problems people could practically solve. The calculator didn’t make math irrelevant, it amplified our ability to apply math to new frontiers in science, engineering, and everyday life. Early computers replaced repetitive tasks but quickly became essential tools for designing, simulating, and communicating in ways previously unimaginable. And the internet, by connecting people and knowledge instantly, transformed how we learn, teach, and work just as it upended traditional barriers of time and place.

How LLMs Revolutionize Learning, Thinking, and Creating

For decades, static search engines like Google helped us find documents, but they offered only snapshots of existing information in the form of fixed pages to read, not living conversations to engage with. Now, large language models (LLMs) like Microsoft CoPilot and ChatGPT have transformed language itself into a dynamic medium for exploration, reflection, and creation. This shift isn’t incremental. It radically redefines how we interact with knowledge, ideas, and each other.

Examples

The following examples illustrate some of the new ways these tools equip us to engage with language, empowering learners and educators alike.

Conversational Exploration

Where static search required carefully crafted queries and sifted documents, LLMs let you ask open-ended questions, refine them on the fly, and receive explanations tailored to your understanding. Learning becomes a dialogue, not a scavenger hunt. This turns curiosity into a powerful driver for discovery.

Semantic Reframing

LLMs can instantly rephrase concepts for different audiences, levels, or rhetorical styles (i.e. simplifying a complex theory for a child or restating a technical report as a persuasive manifesto). This helps educators and students see concepts from multiple angles, deepening comprehension and sparking new insights.

Generative Composition

Beyond retrieving facts, LLMs can synthesize original text combining fragments, styles, or prompts to generate drafts, analogies, or even creative works like speeches or stories. Language becomes a tool for creation, enabling students and professionals to develop ideas iteratively and creatively.

Reflective Dialogue

LLMs can simulate debates, play devil’s advocate, or guide Socratic questioning helping you unpack assumptions, challenge biases, and cultivate critical thinking by exploring perspectives you might never have considered alone.

Iterative Structuring

Whether you need a mind map, timeline, outline, or taxonomy, LLMs can help you scaffold complex topics into organized, actionable structures turning abstract thoughts into concrete plans.

Role-Based Language Interaction

Need to understand a patent case, a legal precedent, or a medical guideline? You can prompt an LLM to “act like an expert” in any field, instantly engaging with specialized language and perspectives that would otherwise require years of experience.

Continuous Remixing

Unlike static search results, you can instantly iterate (shorten, expand, add humor, switch tones, or combine ideas). This supports creative exploration, making language a flexible material or ‘medium’ for innovation.

Multilingual & Cross-Cultural Bridges

LLMs don’t just translate words, they adapt meaning, idioms, and references across languages and cultures, enabling communication and learning that’s both accurate and culturally aware.

Meta-Linguistic Awareness

You can dive into the origins of words, compare expressions across languages, or analyze rhetorical strategies all of which accelerates linguistic and conceptual literacy by exploring language about language.

Simulated Conceptual Playgrounds

LLMs let you invent words, imagine new technologies, or create fictional scenarios bringing a niche creative practice within everyone’s reach.

Why This Matters

Where traditional search gave us snapshots of static information, LLMs enable interactive journeys through language itself. The continuous feedback loop between your curiosity and the model’s responses makes exploration faster, deeper, and more personalized than anything a library or search box ever allowed.

Practical Examples

·       A student struggling with a dense philosophy text can break it down step by step in a dialogue.

·       A manager can draft and iterate policy memos, adjusting tone or complexity instantly.

·       A language learner can practice conversations including slang and idioms in a desired language.

·       A designer can generate hundreds of playful branding ideas by asking the LLM for creative word combinations.

These new methods don’t just make information more accessible, they also invite every learner, thinker, and creator to engage with language as an evolving, participatory process. This is the frontier of education in an AI-powered world.

The Human Producer in the Age of AI

While large language models (LLMs) like CoPilot and ChatGPT can generate vast amounts of text, ideas, and summaries in seconds, these outputs are only as meaningful, accurate, and relevant as the human prompts and revisions that shape them. Far from making humans obsolete, AI tools depend on human skill, insight, and creativity to reach their true potential.

Writing effective prompts is an intellectual act that demands clarity of purpose, deep understanding of the context, and the ability to anticipate ambiguity or bias. It’s a 21st-century literacy skill that is just as important as knowing how to conduct research or craft persuasive arguments. For example, a poorly worded prompt can lead an LLM to produce generic, irrelevant, or even misleading content. A thoughtfully crafted prompt, in contrast, can channel AI’s power to uncover insights or possibilities the human alone might miss.

LLMs can hallucinate facts, reflect biases in their training data, or produce outputs that don’t align with institutional values or professional standards. Humans are essential for fact-checking AI-generated information, contextualizing outputs for specific courses, students, or organizational needs, and upholding ethical guidelines, including privacy, equity, and inclusivity. Like a sculptor refining stone into a statue, the human producer selects, edits, organizes, and integrates AI-generated content with other materials creating lesson plans, communications, strategies, or reports that are coherent, purposeful, and aligned with goals.

AI can suggest, but humans decide what to do. Only humans can weigh competing priorities, anticipate stakeholder reactions, or tailor outputs to the nuanced needs of real people in specific contexts. Rather than seeing themselves as passive recipients of AI outputs, educators, staff, and administrators should embrace their role as producers by actively steering, refining, and integrating AI’s contributions into their unique work. This producer mindset elevates human agency over automation, ensures outputs are fit for purpose, and turns AI into a true partner in productivity and innovation.

The bottom line is that AI doesn’t reduce the need for human expertise. Instead, it raises the stakes for human judgment. Those who master prompt engineering, critical revision, and creative curation won’t be replaced. They’ll lead the way in using AI to create more meaningful, effective, and innovative work.

AI as a Digital Colleague

Instead of seeing AI as a competitor, faculty and staff can treat it as a digital colleague handling repetitive or data-heavy tasks so they can devote time to creative problem-solving, relationship-building, and innovating new services that only humans can provide. This not only preserves jobs but often makes them more meaningful and satisfying.

Examples

The following examples illustrate some of these possibilities.

Admissions & Enrollment

AI chatbots can answer routine questions from prospective students 24/7, freeing staff to build meaningful connections with high-interest applicants or handle complex cases that require empathy and nuance.

Scheduling & Coordination

AI-powered scheduling tools can eliminate the back-and-forth of calendar management for meetings or advising appointments. Staff then gain time for advising conversations that build student trust, something AI can’t replicate.

Data Management & Reporting

AI can automatically compile, clean, and visualize institutional data. Staff can spend more energy analyzing these insights, proposing new strategies, and collaborating with stakeholders to improve student outcomes.

Student Support Services

AI-driven early alert systems can flag patterns of absenteeism or declining grades. Advisors and support staff can focus on proactive outreach and building personalized success plans, enhancing rather than diminishing their roles.

Lesson Planning & Course Design

AI can suggest learning objectives aligned with Bloom’s Taxonomy, recommend active learning strategies, or generate sample syllabi tailored to a course’s level and discipline. This allows educators to spend more time customizing materials for their unique student populations and less time on repetitive planning.

Content Creation & Assessment

AI can draft quiz questions at varying difficulty levels, create practice problems, or generate discussion prompts based on readings. Faculty can then refine these items to ensure alignment with course goals - saving time while maintaining academic rigor.

Lecture Enhancement & Accessibility

AI transcription tools can quickly convert lecture recordings into text for captions or study guides, improving accessibility for students with disabilities and non-native English speakers. Faculty can edit and annotate these transcripts to highlight key points or add clarifying commentary.

Research & Literature Reviews

AI can scan hundreds of academic papers to generate summaries of recent findings on a topic, identify gaps in literature, or suggest emerging trends. Faculty can use these outputs as a starting point for deeper critical analysis or to update their own research agendas.

Feedback & Student Support

AI-powered feedback tools can provide first-pass comments on student drafts (i.e. highlighting grammar, structure, and coherence issues) so faculty can focus on higher-order feedback like argument strength or conceptual understanding.

Course Data & Insights

AI can analyze patterns in student performance across assignments, identify concepts students consistently struggle with, or flag disparities in participation. Faculty can then tailor lectures, add targeted resources, or adjust pacing to better meet student needs.

Using AI Responsibly in Higher Education

While artificial intelligence offers tremendous promise for saving time, enhancing engagement, and unlocking new possibilities in teaching and learning, it also brings serious challenges we cannot ignore. Addressing these concerns openly is essential for ensuring AI supports, not undermines, our educational missions.

Concerns

Data Privacy & Security

AI tools must comply with privacy laws like FERPA, HIPAA, and institutional data policies. Faculty and administrators should work closely with IT and legal teams to vet AI vendors, ensure secure data storage, and establish clear protocols for consent and data sharing. Where possible, favor tools that process data locally or anonymize sensitive information.

Intellectual Property & Academic Integrity

Institutions need updated academic integrity guidelines clarifying appropriate vs. inappropriate AI use for both students and educators. Faculty should discuss with students when and how AI assistance is acceptable, and cite AI-generated content transparently. Meanwhile, institutions should establish clear policies on intellectual property ownership for AI-assisted works.

Accessibility & Digital Divide

Equitable AI integration requires ensuring all students and faculty have access to devices, internet, and AI literacy resources. Institutions can expand laptop loan programs, offer on-campus AI workshops, and design policies that recognize and address the barriers some learners face.

Algorithmic Bias & Cultural Context

Bias in AI can perpetuate systemic inequities. Faculty and staff should critically evaluate AI outputs, encourage diverse perspectives, and teach students to question the assumptions embedded in algorithmic tools. Institutions can also advocate for AI vendors to make their models more transparent and inclusive.

Cognitive Offloading & Skill Atrophy

To avoid undermining essential skills, AI use should complement, not replace, core learning activities. Faculty can design assignments requiring students to justify, critique, or improve AI-generated outputs, reinforcing critical thinking, research, and writing abilities.

Impact on Critical Pedagogy & Creativity

AI can inadvertently steer education toward conformity if used uncritically. Educators should incorporate AI in ways that preserve open-ended inquiry, exploration of multiple perspectives, and space for students to wrestle with complexity as these are hallmarks of transformative learning.

Charting New Horizons

In every era of transformative change, those who thrive are not those who cling to past ways of working, but those who adapt, evolve, and push the boundaries of what’s possible. Today, artificial intelligence (AI) marks a turning point as significant as the Industrial Revolution or the dawn of the internet, and higher education cannot afford to stand still. Technologies like large language models, AI tutors, and predictive analytics are developing at exponential rates. Standing still doesn’t keep us in place - it means falling behind. To serve students effectively and remain relevant, we must embrace change as a constant.

True breakthroughs don’t happen at the center of what is comfortable. They happen at the edge, where new tools, bold ideas, and emerging needs intersect. By experimenting with AI to personalize learning, streamline processes, or uncover new patterns in data, faculty and administrators can discover opportunities to transform education in ways we can’t yet fully imagine. The tasks that defined our jobs yesterday may no longer be the most valuable today. By adapting our roles to prioritize creativity, critical thinking, and human-centered problem-solving (skills AI can’t replicate) we ensure our work remains not only relevant, but deeply impactful (World Economic Forum, 2023).

In a world where students will graduate into careers shaped by AI, automation, and continual disruption, we must model lifelong learning, adaptability, and curiosity. Our willingness to explore beyond the edge inspires them to do the same and equips them with the mindsets they’ll need to thrive. Settling for what’s familiar may feel safe in the short term, but it creates risk in the long term for individuals and institutions. By seeking the edge, we stay attuned to new possibilities and proactively shape the future of education, rather than being shaped by it.

AI invites us to rethink what we do, how we do it, and what new horizons we can reach. By embracing adaptation, actively evolving our roles, and seeking the frontier of what’s possible, we honor both our callings as educators and administrators and our responsibility to prepare students for a rapidly changing world.

Conclusion

AI is not a passing trend - it’s a defining force of our time. Like calculators, computers, and the internet before it, AI (particularly large language models) offers us powerful new tools to amplify what humans do best: think creatively, connect meaningfully, and solve complex problems. However, realizing this potential requires each of us to step beyond our comfort zones. It means reframing AI as a partner in teaching, learning, and administration; seeing ourselves not as passive recipients of automation, but as producers who shape, refine, and innovate with these tools. It means acknowledging that while fears of change are natural, the risks of standing still in a rapidly evolving world are far greater.

By recognizing and proactively addressing legitimate concerns, we can chart a path toward responsible, ethical, and inclusive AI adoption. This balanced approach ensures AI strengthens, rather than weakens, our shared commitment to high-quality, equitable, and meaningful learning experiences. As educators, staff, and leaders, we owe it to ourselves, and to our students, to model curiosity, adaptability, and a willingness to find the edge of what’s possible. By doing so, we not only future-proof our own work, but we also help prepare our students for a world where AI will be woven into every field and profession.

Quick Start Guide

Start today: Choose one class, project, or workflow. Ask - How could an AI tool help me save time, reach more students, or spark creativity? Try it, reflect, and share your experience with a colleague because change is easier when we walk together.

Step 1. Reflect on Needs and Goals
Ask yourself:

• Where do I spend significant time on repetitive or administrative tasks?
• What teaching or service challenges could AI help me address?
• What outcomes do I hope to improve (e.g., engagement, accessibility, efficiency)?

Step 2. Choose a Low-Risk Use Case
Start small by picking a simple, well-defined task that won’t affect high-stakes decisions such as:

• Drafting a syllabus outline or email.
• Creating quiz questions from a reading.
• Summarizing research articles.

Step 3. Learn & Test the Tool

• Select an AI tool vetted by your institution or IT team.
• Explore tutorials or brief demos.
• Test it privately before using it with students or sharing outputs.

Step 4. Revise & Curate Outputs
Remember: AI-generated content needs your judgment.

• Fact-check for accuracy.
• Revise for clarity, tone, and alignment with your goals.
• Adapt examples for your course or campus context.

Step 5. Check Privacy & Ethics
Before entering student or sensitive data into an AI tool:

• Review institutional policies.
• Ensure compliance with FERPA, HIPAA, or other relevant regulations.
• Prefer tools that anonymize data or process it locally when possible.

Step 6. Pilot with Students or Colleagues
Use your curated outputs:

• In a class, with transparency about AI use.
• As a draft shared with a trusted colleague for feedback.
• In team meetings to demonstrate potential benefits.

Step 7. Reflect & Iterate
Consider the following after your first experiments:

• What worked well?
• What would you do differently?
• How did students or colleagues respond?

Use these insights to plan your next, more ambitious AI integration.

Step 8. Share & Collaborate

• Join or start a faculty/staff learning community focused on AI.
• Exchange tips, challenges, and ideas.
• Advocate for institutional support, training, and responsible policies.

By starting small, reflecting critically, and collaborating with peers, educators and staff can responsibly harness AI to enhance their work while safeguarding quality, ethics, and student success.

References

Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted/

Bessen, J. E., Goos, M., Salomons, A., & Van den Berge, W. (2019). Automatic reaction: What happens to workers at firms that automate? Brookings Institution.
https://scholarship.law.bu.edu/cgi/viewcontent.cgi?params=/context/faculty_scholarship/article/1585/&path_info=2022_update___What_Happens_to_Workers_at_Firms_that_Automate.pdf

Bridges, W. (2009). Managing transitions: Making the most of change (3rd ed.). Da Capo https://www.scribd.com/document/371817944/Managing-Transitions-25th-Anniversary-Edi-William-Bridges

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company. https://wwnorton.com/books/9780393239355

Kim, N. J., & Kim, M. K. (2022). Teacher’s perceptions of using an artificial intelligence-based educational tool for scientific writing. Frontiers in Education, 7, Article 755914. https://doi.org/10.3389/feduc.2022.755914

World Economic Forum. (2023). The future of jobs report 2023. https://www.weforum.org/publications/the-future-of-jobs-report-2023/