AI-Inclusive Assignments: Teaching Critical Engagement with Technology
Generative AI is no longer a distant disruption; it is a present reality transforming our classrooms across every discipline. Students are using tools like ChatGPT, Claude, and Gemini to brainstorm, summarize, research, and even generate entire assignments. For faculty, this technological shift presents an urgent pedagogical challenge: how do we redesign assignments so that students are still learning, thinking critically, and demonstrating authentic engagement with course material?
While these challenges span all academic fields, from literature and history to engineering and business, I’ll explore this framework through the lens of journalism education, my own discipline. Journalism programs face particularly acute pressures in the AI era, as our students will enter newsrooms where AI tools are already being integrated into daily workflows for research, fact-checking, and even content generation. The skills we teach—critical thinking, source verification, ethical decision-making, and compelling storytelling—become even more crucial when AI can produce polished but potentially inaccurate or biased content at lightning speed.
The framework I propose can be adapted across disciplines, but journalism education offers compelling examples of how faculty can thoughtfully integrate or resist AI while maintaining academic rigor and preparing students for their professional futures.
I suggest approaching this challenge through a two-pronged strategy. Some assignments can be redesigned to include AI as a legitimate learning tool, teaching students to work with these technologies responsibly. Others can be structured to make reliance on AI less useful or entirely irrelevant. By categorizing assignments as AI-Inclusive or AI-Resistant, faculty can make strategic decisions that align with their teaching philosophy, course objectives, and student needs.
1. AI Collaboration & Critique: Building Editorial Skills
This approach transforms AI from a crutch into a collaborative partner that students must critically evaluate and improve. Students begin by generating an AI draft on a journalism topic, then engage in rigorous critique and revision—skills that mirror the editorial process they’ll encounter in professional newsrooms.
Journalism Example: In a media law course, students might ask ChatGPT to write an article about recent changes in shield law protections for journalists. They then must fact-check every claim, identify legal inaccuracies, verify case citations, and interview actual legal experts to correct errors. Students annotate the AI draft to highlight problematic generalizations, missing context, or oversimplified explanations of complex legal concepts.
The revision process requires students to integrate authoritative sources, add nuanced analysis, and ensure the article meets journalistic standards for accuracy and fairness. This exercise teaches students that AI can provide a starting framework, but professional journalism demands verification, expertise, and critical judgment that AI cannot provide.
Learning Objectives: Students develop fact-checking skills, learn to identify AI limitations in specialized topics, and practice the editorial judgment essential in professional journalism. They also gain experience in source verification and learn to distinguish between surface-level information and the deep reporting that characterizes quality journalism.
2. Compare & Contrast Human vs. AI Work: Developing Editorial Judgment
This strategy positions AI as an object of study rather than a shortcut, helping students develop the analytical skills essential for editorial decision-making in modern newsrooms.
Journalism Example: Provide students with an AI-generated news article about a local government meeting alongside a human-written piece covering the same event by a professional journalist. Students analyze both pieces for accuracy, sourcing, narrative structure, and ethical considerations. They examine questions like: Which piece better captures the significance of policy changes? How do the sources differ? What context does the human journalist provide that AI missed? Where does AI excel in organizing information, and where does it fall short in understanding community impact?
Students then write a comparative analysis explaining which piece better serves the public interest and why. This assignment can be extended by having students interview the human journalist about their reporting process, creating a deeper understanding of how professional news judgment operates.
Learning Objectives: Students sharpen their ability to evaluate news quality, understand the value of human expertise in journalism, and develop criteria for assessing AI-generated content in professional contexts.
3. Reflection & Transparency Assignments: Building Ethical Frameworks
These assignments transform AI use from a hidden practice into a subject of ethical inquiry, helping students develop frameworks for responsible AI use in their future careers.
Journalism Example: After completing a research project on media bias, students submit a detailed reflection describing any AI tools they used, how they used them, and the ethical considerations they weighed in making those decisions. They might explore questions like: When is it appropriate for journalists to use AI for research? How might AI bias affect news coverage? What transparency obligations do journalists have regarding AI use? Students could also interview working journalists about their newsroom’s AI policies and compare different organizations’ approaches.
This reflection component could be expanded into a policy proposal where students draft ethical guidelines for AI use in student media organizations, considering both the benefits and risks of these technologies.
Learning Objectives: Students develop ethical reasoning skills, practice professional reflection, and engage with emerging questions about AI transparency in journalism that they’ll face throughout their careers.
AI-Resistant Assignments: Emphasizing Authentic Human Skills
While AI-Inclusive strategies offer valuable learning opportunities, there are times when faculty need to design assignments that minimize AI’s usefulness. AI-Resistant assignments don’t assume students won’t use AI; instead, they make it much harder for AI to replace authentic student thinking and engagement. These approaches emphasize elements that AI cannot easily replicate: personal experience, iterative process work, real-time demonstration, and deeply contextualized local knowledge.
4. Process-Oriented Submissions: Documenting the Journey of Learning
Rather than evaluating only final products, these assignments require students to document their learning journey, making visible the critical thinking and skill development that occurs during the reporting and writing process.
Journalism Example: For a investigative reporting project, students submit a comprehensive portfolio including: initial story pitch with justification, source contact logs with reflection on access challenges, interview transcripts with analysis of follow-up questions needed, fact-checking documentation with verification methods, story outline evolution showing how their angle developed, and draft revisions with explanations of editorial choices.
This approach mirrors the documentation that professional journalists maintain for complex stories and legal protection. Students might also include reflection on dead ends and failed story angles, helping them understand that investigative journalism often involves extensive work that doesn’t appear in the final piece.
Learning Objectives: Students develop professional documentation habits, learn to articulate their reporting methodology, and demonstrate the critical thinking skills that distinguish journalism from content generation.
5. Oral Defenses and Newsroom Simulations: Demonstrating Real-Time Expertise
Pairing written work with oral presentations or simulated professional scenarios ensures students can articulate and defend their work in real-time—a skill essential for newsroom environments where journalists must explain their reporting decisions to editors and the public.
Journalism Example: After submitting an investigative piece, students participate in a “newsroom defense” where they present their story to classmates playing the roles of editor, fact-checker, and legal counsel. They must defend their source selection, explain their verification methods, and respond to challenges about potential legal issues or ethical concerns. This simulation mirrors the actual editorial process in professional newsrooms and requires students to demonstrate a comprehensive understanding of their work.
Alternatively, students might participate in a “press conference” simulation where they must answer questions about their reporting from classmates playing community members, officials, or other journalists. This format tests whether students truly understand the implications and context of their reporting.
Learning Objectives: Students develop presentation skills crucial for journalism careers, practice defending editorial decisions, and demonstrate mastery of complex information in dynamic settings.
6. Hyper-Local and Personal Experience Assignments: Leveraging Unique Context
These assignments draw on contexts uniquely familiar to students—local events, campus issues, or personal experiences—that AI cannot access or authentically represent.
Journalism Example: Instead of a generic analysis of social media’s impact on news consumption, students investigate how their specific campus community gets news during crisis situations. They might interview dormitory residents about information sources during a recent campus emergency, analyze their university’s crisis communication through student social media posts, or examine how local student media covered a recent campus controversy compared to regional newspapers.
Another approach involves “beat reporting” where students are assigned specific local beats (city council, school board, campus sustainability initiatives) and must develop sources, attend meetings, and produce ongoing coverage. AI cannot replicate the relationship-building and institutional knowledge that develops through sustained local reporting.
Learning Objectives: Students develop source-building skills, learn community journalism practices, and create work that reflects genuine engagement with local issues and stakeholders.
Additional Interdisciplinary Assignment Redesign Ideas
Here are more strategies you might consider exploring. Each could be adapted across disciplines:
Data or source-based assignments using original datasets, surveys, or interviews. AI can help with analysis, but students still need to gather and interpret unique evidence.
Multi-modal submissions (mix of video, infographic, and written work). AI may assist with one component, but students must integrate multiple modes of communication.
In-class writing or timed problem-solving to demonstrate independent skill. Balances take-home assignments with authentic, real-time student work.
Portfolio-based assessment across a semester to track growth. Harder to fake because it requires sustained, iterative engagement over time.
Peer review integration with structured student feedback. Encourages interaction with authentic student writing and builds evaluative skills.
Low-stakes, frequent assignments to replace a single high-stakes project. Less incentive to outsource and provides multiple checkpoints for learning.
AI detection + transparency policies (grading partly on honesty about use). Shifts emphasis from policing to accountability and reflection.
Scenario-based or role-play assignments (e.g., writing as a policymaker or executive). Requires creativity and contextual reasoning beyond generic AI output.
Community or experiential projects (service learning, ethnography, fieldwork). Anchors learning in real-world applications that AI cannot replicate.
Concept maps & visual thinking assignments to show connections between ideas. AI can generate diagrams, but nuanced student connections are harder to fake.
Annotated bibliographies with commentary on how each source connects. AI often struggles with accurate citations and meaningful integration.
Journaling or ongoing learning logs documenting thought processes. Demonstrates authentic engagement and reflective practice.
AI-resistant formats like handwritten essays, posters, or whiteboard solutions. Limits the usefulness of AI-generated text while emphasizing student voice.
Public-facing assignments such as blogs, podcasts, or campaigns with real audiences. Students are more invested when their work is visible beyond the classroom.
Conclusion: Preparing Students for an AI-Integrated Future
Redesigning assignments in the age of AI doesn’t require an all-or-nothing approach. Faculty across disciplines can decide whether to incorporate AI as a tool to be interrogated and refined, or to structure assignments that emphasize authentically human skills like relationship-building, real-time critical thinking, and deep contextual understanding.
For journalism educators specifically, this moment presents an opportunity to reinforce the irreplaceable value of human judgment, ethical reasoning, and community engagement in news production. By choosing between or thoughtfully combining AI-Inclusive and AI-Resistant approaches, we can adapt our courses to today’s technological reality while preserving and strengthening the critical thinking skills that define quality journalism.
The goal is not to eliminate AI from our students’ futures; they will work with these tools throughout their careers. Instead, our responsibility is to ensure they enter the profession equipped with the judgment, skills, and ethical frameworks necessary to use AI responsibly while maintaining journalism’s core commitment to truth, accuracy, and public service.
As we navigate this transition, the most important question isn’t whether our students will use AI, but whether we’ve prepared them to use it wisely, transparently, and in service of the democratic values that journalism exists to protect.
This article originally appeared in the Kropp on Campus LinkedIn Newsletter on September 23, 2025.