Why Frontline University Faculty Struggle to Adapt Teaching for a World with Gen

1. 4 Key Considerations When Evaluating Teaching Options for AI-era Classrooms When departments debate how to respond to generative AI, discussion often drifts toward tools or detection technologies. What matters most are deeper questions that shape any sustainable change. Ask these four first: Learning goals: Are you assessing factual recall, higher-order thinking, or professional practice? The type of learning outcome determines which methods remain valid when AI can generate polished text or code. Transparency and fairness: How will new rules affect students with different access to technology, language skills, or disability accommodations? Policies that look strict on paper can create inequities in practice. Scalability and workload: What can instructors reasonably implement and sustain across large courses or multiple sections? Faculty time and department resources are often the real constraint. Pedagogical fit: Does a proposed change align with disciplinary norms and accreditation expectations? An approach that works in studio art may not translate to organic chemistry. In contrast to quick technology fixes, approaches that start with these considerations tend to produce coherent, defensible strategies. Keep them visible when you compare options below. Relying on Traditional Lectures and Written Exams: What Still Works and What Breaks Many instructors began by doubling down on established methods: more proctored exams, stricter plagiarism policies, and lecture-driven content delivery. These responses are understandable. They preserve familiar measures of rigor and preserve comparability across semesters. They also reveal clear limits. Strengths of traditional approaches: Clarity: Students and faculty know the rules and expectations. Efficiency: Multiple-choice and short-answer exams scale well for large enrollments. Documentation: Conventional artifacts make it easier to demonstrate compliance with program standards. Where traditional methods break down: Surface-focused assessment becomes easy to game. Generative AI can produce credible essays, code, and problem solutions that meet surface criteria without demonstrating deep understanding. Faculty workload increases when trying to police dishonesty. Manual checking, chasing cases, and adjudication consume time that could be spent on improvement. Student learning can suffer. If assessments emphasize final products, instruction may drift toward polishing outputs rather than developing process skills like critique, iteration, or source evaluation. In contrast to new designs, sticking with traditional formats often results in a reactive posture: more enforcement and less learning- oriented change. Some courses, especially those testing foundational factual knowledge under timed, proctored conditions, can still rely on traditional methods with minor adjustments. For most courses though, the risks of misalignment between assessment and AI capabilities are real. Designing Assessments for AI-augmented Classrooms: Authentic Tasks and Process-focused Work Alternative assessment strategies aim to shift emphasis from polished final products to the process of learning. These approaches accept that students will use powerful tools and instead evaluate how students use them, what choices they make, and how they reflect on their decisions. Key features of this modern approach: Authentic tasks: Assignments mirror real-world professional work, requiring judgment, negotiation of constraints, or situated argumentation that AI alone cannot resolve convincingly. Process documentation: Students submit drafts, research logs, annotated prompts, or version histories showing how work evolved. Assessment therefore values iteration, not just the

2. end result. Reflective components: Short reflective memos or oral defenses ask students to explain what they did, why, and how they used tools—this is often harder to fake convincingly. Rubrics aligned to process and metacognition: Rubrics that reward evidence of critical evaluation, source selection, and synthesis provide clearer targets than rubrics that reward surface features of writing. Pros of process-focused design: Robust to AI: When assessment privileges reasoning steps, critical choices, and contextual knowledge, AI-generated responses are less likely to succeed on their own. Improves transfer: Students who learn how to use tools responsibly and think through decisions are better prepared for professional work. Promotes academic integrity constructively: Instead of policing, instructors teach responsible use and evaluate it. Cons and practical challenges: Higher faculty time investment: Grading drafts and reflections requires more touchpoints. This can be mitigated with peer review and pass/fail milestones. Need for clear scaffolds: Students unfamiliar with these expectations need examples, templates, and feedback early in the course. Scalability in large courses is a hurdle, though technology and structured peer assessment models can help. On the other hand, for smaller seminars, capstones, or project-based courses, process-focused assessment often aligns naturally and can raise both learning and integrity. For lecture-heavy large courses, hybrid models can introduce process elements into selected high-stakes assignments to balance workload. Other Viable Options: Open-book Exams, Oral Assessments, Peer Review, and Direct AI Integration Departments often explore several secondary strategies. Each has trade-offs; selecting among them requires aligning with the key considerations above. Open-book and Open-note Exams Open-book formats reduce the value of memorization and shift emphasis to application and analysis. They pair well with questions that require synthesis, interpretation, or novel problem solving. Pros: Lower incentives to cheat, better alignment with real-world tasks, manageable for large cohorts with well-designed questions. Cons: Crafting questions that reliably discriminate levels of understanding takes skill. Time-limited online conditions can raise access concerns. Oral Examinations and Defenses Short oral exams or viva-style checkpoints probe understanding directly. They are effective at confirming student knowledge, especially around key concepts. Pros: Harder to fake, immediate feedback, clarifies misunderstandings. Cons: Time-intensive, scheduling challenges, may disadvantage non-native speakers without supports. Structured Peer Assessment Peer review distributes grading load and develops critical evaluation skills. When paired with calibration exercises and rubrics, it yields reliable results. Pros: Scales well, teaches meta-skills, reduces single-instructor burden. Cons: Quality depends on training and incentives. Some students remain skeptical of peer fairness. Explicit AI Integration: Teach Prompting and Tool Use

3. One option is to bring generative AI into the classroom explicitly. Assignments can require students to show prompts used, critique AI output, or improve generated drafts. Pros: Prepares students for actual practice, allows assessment of tool use as a skill, reduces the adversarial dynamic. Cons: Requires teaching prompt literacy and evaluation skills. Risk of normalizing superficial acceptance of AI output without critical checks. Approach Strength Constraint Traditional timed exams Clear, scalable Vulnerable to AI; surface learning Process-based assessment Resilient to AI; deep learning Higher faculty time cost Open-book exams Tests application Requires careful question design Oral exams Hard to fake Not scalable without resources AI integration Teaches real-world skills Requires training and policy clarity Choosing an Approach That Fits Your Department and Course Goals Decisions should be pragmatic, iterative, and aligned with the four key considerations. Use the short self-assessment below to identify a starting point for your course or program. Self-assessment: Which path should your course take? What is the primary learning outcome? (A) Factual recall, (B) applied analysis, (C) research and synthesis. Class size and instruction model: (A) Large lecture, (B) medium seminars, (C) small seminars or labs. Resource availability: (A) Minimal faculty time, (B) moderate TA support, (C) high faculty involvement. Accreditation/discipline constraints: (A) High need for standardized measures, (B) moderate, (C) flexible. Student equity concerns: (A) Many with limited tech access, (B) mixed, (C) mostly well- resourced. Scoring guide: Give 1 point for each "A", 2 for each "B", 3 for each "C". Total 5-15. Score 5-8: Favor scalable approaches. Tighten traditional assessments where appropriate, use well-designed open-book exams, and adopt selective process elements for major assignments. Invest in short orientation modules on academic integrity. Score 9-12: A hybrid model suits you. Combine process-based assessment with peer review, include oral checkpoints for key deliverables, and pilot AI-integration assignments in one module. Score 13-15: Your course can embrace deep process assessment. Require drafts, reflective memos, and authentic projects. Train students in responsible AI use and use rubrics that reward decision-making. Practical rollout checklist for departments Choose pilots carefully: Start with 1-2 courses across different levels to test new models. Offer faculty development: Short workshops on crafting application-focused questions, designing rubrics for process, and running calibrated peer review sessions. Communicate to students: Publish clear policies and examples of acceptable tool use. Include prompt documentation where relevant. Monitor equity: Track access issues and provide alternatives when technology creates barriers. Evaluate and iterate: Collect student feedback, analyze grades and integrity incidents, and refine approaches each term. In contrast to quick policy edicts, this kind of phased, supported change reduces faculty resistance and improves buy-in. Departments that front-load communication and training report fewer disciplinary cases and better alignment between learning goals and assessment artifacts. Short Quiz: How Ready Is Your Course for AI-aware Teaching? Do your current assessments require demonstration of process (drafts, logs, or reflections)? (Yes/No) Have you provided at least one assignment that asks students to evaluate or improve AI-generated material? (Yes/No) Can your grading model incorporate peer-assessed components with calibration? (Yes/No) Is there a department-level guideline on acceptable AI use that students see at the start of the term? (Yes/No) Do you have TA or institutional support for grading process-based work? (Yes/No) Scoring: Each Yes = 1 point. 0-1: Not ready; prioritize clear policy and a single, low-cost pilot. 2-3: Partially ready; build scaffolds and pilot AI-integration in one module. 4-5: Ready to scale; formalize training and expand process-based assessment.

4. Concluding Guidance: Balance, Experimentation, and Careful Communication Faculty struggle because the task is not only technical. It is pedagogical, political, and logistical. Responses that ignore learning outcomes, that lean exclusively on policing, or that assume one-size-fits-all solutions produce frustration. In contrast, strategies that begin with learning goals, weigh fairness and feasibility, and iterate through pilots are more likely to succeed. Departments should treat this as a human-centered teaching methods medium-term curriculum question, not an emergency compliance problem. Provide concrete support for instructors who want to redesign assessments. Test variations at small scale before committing. Include students in conversations so policies feel legitimate rather than punitive. Over time, a mixed portfolio of approaches - traditional where appropriate, process-focused where needed, and explicit tool training across the curriculum - will serve both learning and integrity goals. Finally, remember that the ultimate aim is educational: to help students gain durable skills for a world where tools will keep changing. When assessment focuses on judgment, iteration, and ethical use of technology, faculty find they are not merely policing new tools but preparing students for them.