📖 Module 3: Essential Resources
GenAI Prompting
How to Use This Resource Library
This resource library is organized into three tiers — Essential, Recommended, and Optional — to help you plan your available class time and meet your students’ needs. Essential materials form the core of the module and directly address the learning outcomes. Recommended materials add depth. Optional materials extend the module or offer additional support to students.
The materials below are the Essential resources for Module 3. There are three sections: Prompting GenAI, Evaluating Output, and GenAI in Your Discipline. Each entry includes a brief description to help you decide how and when to use it.
Section 1: Prompting GenAI — Ethical and Effective Uses
💻 Instructor Preparation
📚 Readings
Using GenAI Right Now: A Quick Guide (One Useful Thing, Ethan Mollick) 🔗 [https://www.oneusefulthing.org/p/using-ai-right-now-a-quick-guide]
Mollick’s most current overview of the major GenAI platforms and how to use them effectively. A strong starting point for instructors who want to get up to speed quickly before teaching this module.
Getting Started with GenAI: Good Enough (One Useful Thing, Ethan Mollick) 🔗 [https://www.oneusefulthing.org/p/getting-started-with-ai-good-enough]
A practical, low-barrier introduction to getting started with GenAI and prompting. Useful for instructors who are new to prompting or who want a clear, current framework before teaching prompt writing strategies.
📽️ Videos
Practical GenAI for Teachers and Students (Ethan and Lilach Mollick, YouTube Playlist, each video ~10-13 min.) 🔗 [https://www.youtube.com/watch?v=t9gmyvf7JYo&list=PLWO-2_i10ZOt6uNiZCKx8uMkmGzfWzT7y]
A 5-part video series by Ethan and Lilach Mollick offering practical guidance on GenAI as a teaching tool. Covers a range of topics including how to incorporate GenAI productively in the classroom. Part III focuses specifically on prompting and is particularly relevant to this module.
📝 Student Materials
📚 Readings
Prompt Writing Guide (Teachers College, Columbia University) 🔗 [https://www.tc.columbia.edu/digitalfuturesinstitute/learning–technology/instructional-guides–resources/self-paced-learning-guides/ai-in-education-guides-tips-and-tricks-for-prompt-writing/]
An academic resource breaking down eight key tips for prompt writing, including the “Act as if…” persona technique and using Do/Don’t instructions to shape GenAI responses. Includes a 26-minute masterclass video on prompting. Can be assigned as pre-class reading or used during class to introduce the prompt formula.
Creating Effective Prompts (Monash University) 🔗 [https://www.monash.edu/student-academic-success/learning-with-ai/practical-skills/creating-effective-prompts]
An excellent resource for college students that explains prompt chaining — breaking complex tasks into smaller, sequential steps — and how to narrow down topics for more useful GenAI research assistance. A strong companion to the hands-on prompting activity in the Module 3 lesson plans.
The Ultimate Guide to Prompt Engineering (Lakera) 🔗 [https://www.lakera.ai/blog/prompt-engineering-guide]
A more technical but clearly written guide comparing how different GenAI models — including Gemini and Claude — respond to specific types of instructions. Useful for students who want to go deeper on prompting strategies or who are ready to work across multiple platforms.
Section 2: Evaluating Output
💻 Instructor Preparation
📚 Readings
SIFT: A Framework for Evaluating Information (University of Chicago Library) 🔗[https://guides.lib.uchicago.edu/c.php?g=1241077&p=9082322]
A library research guide introducing the SIFT method — Stop, Investigate the source, Find better coverage, Trace claims to original context — as a framework for evaluating online information and GenAI outputs.
The SIFT Strategy: A Four-Step Method for Spotting Misinformation (BBC Future) 🔗[https://www.bbc.com/future/article/20240509-the-sift-strategy-a-four-step-method-for-spotting-misinformation]
Explains the SIFT method pioneered by digital literacy expert Mike Caulfield. Accessible and well-written. Useful both as instructor background reading and as a student-facing resource. Can be assigned or projected during class.
The CRAAP Test (LOEX Quarterly) 🔗 [https://commons.emich.edu/loexquarterly/vol31/iss3/4]
The original academic article introducing the CRAAP Test — Currency, Relevance, Authority, Accuracy, Purpose — as a framework for evaluating information sources. Useful instructor background for understanding the framework before teaching it.
CRAAP Test (craaptest.net) 🔗[https://craaptest.net]
A concise, student-friendly web resource explaining the CRAAP Test with clear definitions and examples. Can be shared directly with students or used as a quick in-class reference.
📝 Student Materials
📽️ Videos
Why You Need to Evaluate GenAI Outputs (Georgian College Library ~5 min.) 🔗 [https://www.youtube.com/watch?v=o_vW4MCbXiU]
A clear, student-friendly video explaining why GenAI outputs must be carefully evaluated before use, addressing concerns about fabrication, plagiarism, and accuracy. Can be shown in class before the output evaluation activity or assigned as homework.
📚 Readings
How to Evaluate GenAI Outputs for Accuracy and Bias (Dharmendra Pratap Singh, Medium) 🔗 [https://medium.com/@dharamai2024/%EF%B8%8F-how-to-evaluate-ai-outputs-for-accuracy-quality-and-bias-a488b298ea45]
A student-friendly breakdown of four core metrics for evaluating GenAI outputs: Accuracy, Coherence, Relevance, and Tone Alignment. A practical companion to the 4-Question Check framework introduced in the Module 3 lesson plans.
Help Students Be Critical of GenAI (University at Albany) 🔗 [https://www.albany.edu/teaching-and-learning/teaching-resources/help-students-be-critical-ai]
Provides three structured classroom experiences for different disciplines — Social Sciences, Education, and Art — designed to build students’ critical evaluation skills around GenAI outputs. Adaptable for a range of FYS contexts.
Identifying GenAI Bias and Misinformation (Aralia Education) 🔗 [https://www.aralia.com/helpful-information/how-to-identify-ai-bias-and-misinformation-with-examples/]
A practical guide for students on spotting red flags in GenAI outputs, including overly absolute claims, vague statements without evidence, and other hallmarks of unreliable or biased content.
Section 3: GenAI in Your Discipline
As each discipline is differently impacted by GenAI, instructors will need to adjust their approach. The framework below is designed to help you think through the questions and angles most relevant to your field. The Student Activities document provides in-class activity options for exploring how GenAI impacts various disciplines and careers. We also recommend that instructors conduct their own research on GenAI’s specific impact in their field before engaging students with this topic.
💻 Instructor Preparation
A Framework for Teaching GenAI in Your Discipline
Use the following three lenses to shape how you approach GenAI in your FYS course:
🔎 Lens 1: GenAI as Subject Matter
What should students in my discipline know about GenAI itself?
- Computer Science: Technical implementation, algorithms, ethics
- English: GenAI as author/co-author, questions of creativity and voice
- History: GenAI in historical research, preservation, contextualization
- Biology: GenAI in genomics, drug discovery, pattern recognition
- Business: GenAI in decision-making, automation, competitive strategy
- Art: GenAI as creative tool and collaborator, questions of authorship
- Psychology: GenAI and human cognition, therapy bots, bias in diagnosis
🔎 Lens 2: GenAI as Tool
How is GenAI being used — or could be used — as a tool in my field?
- What professional tasks does GenAI assist with?
- What research methods does GenAI enable or change?
- What new questions can we ask because of GenAI?
🔎 Lens 3: GenAI as Disruptor
What fundamental questions does GenAI raise about my discipline?
- What assumptions are being challenged?
- What skills remain uniquely human?
- What ethical questions emerge?
📚 Additional Resources: GenAI’s Impact on Careers and the Labor Market
Scaffolding GenAI Literacy to Boost Student Employability (AACSB Insights, December 2025) 🔗 [https://www.aacsb.edu/insights/articles/2025/12/scaffold-ai-literacy-boost-student-employability]
Argues that GenAI has structurally changed entry-level hiring by eliminating many junior-level tasks, effectively removing the bottom rungs of the career ladder. Useful for grounding discussions about why GenAI literacy matters for students’ professional futures.
Beyond the Buzz: Developing the GenAI Skills Employers Actually Need (Lightcast Labor Market Analysis) 🔗 [https://lightcast.io/resources/research/beyond-the-buzz-developing-the-ai-skills-employers-actually-need]
An analysis of 1.3 billion job postings finding that GenAI skills command a 28% salary premium — approximately $18,000 per year. Key findings: 51% of GenAI job postings are now outside the tech sector, with 800% growth in non-tech generative GenAI roles since 2022. Introduces the “GenAI Skills Disruption Matrix” plotting skills by growth, value, and GenAI exposure.
The State of GenAI (McKinsey Global Survey, Annual) 🔗 [https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai]
McKinsey’s annual global survey tracking GenAI adoption in organizations. 2024 findings: 72% of organizations now use GenAI (up from 55% in 2023); 65% regularly use generative GenAI (doubled from 33%); half use GenAI across two or more business functions. Useful for grounding discussions about the pace of workplace change.
Nearly Half of Hiring Managers Say GenAI Is the Most Important Skill to Have on a Resume in 2025 (Resume Builder Survey) 🔗 [https://www.resumebuilder.com/resume-examples/nearly-half-of-hiring-managers-say-ai-is-the-most-important-skill-to-have-on-your-resume-in-2025]
A survey of 1,000 hiring managers finds GenAI abilities — including machine learning and generative GenAI tools — are now the top hard skill employers seek, identified by 47% of respondents. Top soft skills: problem-solving (68%), communication (65%), and time management (64%).
