Practical Responses to AI

Promoting AI Literacy

AI’s impact on higher education extends far beyond concerns of cheating and non-learning. While plagiarism and ethics issues are very real, the advance of generative AI also created an opportunity for educators to focus on the challenges of cheating and work out strong solutions. Another important consideration is the changing job markets. For many fields and professions, digital fluency and proficient use of generative AI have become essential competencies that employees are expected to use every day on the job. For these reasons, it is crucial to integrate the basics of AI literacy in college curriculum. Please read the "Definitional Frameworks for Foundational, Intermediate, and Advanced AI Skills" document developed by Montclair AI Working Group's Curricular Development Subcommittee in collaboration with the AI Competency and Fluency Subcommittee for details on these competencies to guide faculty, students, and staff.

Work is currently underway to create a graduation requirement at Montclair to ensure that all our students are provided with foundational knowledge they need to use AI safely and effectively. While that work is in progress, instructors are encouraged to implement course policies and pedagogical approaches that promote AI literacy among students and assess student learning in plagiarism-resistant ways.

• Develop a course policy on AI use. Continue discussing the relevant aspects of the policy throughout the semester.
  • Students are likely facing major differences in faculty opinions about AI. Whether or not you allow AI in your classroom, be very clear about your expectations.
  • Beginning Fall 2026, faculty will have the option on Montclair Syllabus to choose between three pre-drafted course policies on AI use, or write their own.
• Teach your students to use AI safely. Personal and sensitive information, as well as any unpublished work in its entirety, should never be entered into AI chatbots. 
• Teach your students to cite AI correctly.
• Ask your students to examine AI outputs critically.
• Encourage students to learn more about ethical uses of AI: personalizing learning, enhancing productivity, streamlining routine tasks that do not involve concerns of privacy or authorship.
• For more ideas, see Teaching with ChatGPT: Assignment Design Tips and Ideas.

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Mitigating Cheating and Non-Learning

Using AI without proper acknowledgement to create or enhance submissions when an assignment does not explicitly call for it is academically dishonest. It is not, in fact, fundamentally different from having another person write your paper, take your test, or complete your assignment. While it may be difficult to come up with assignments that are completely AI-proof, the tips below can help you make your classroom more resistant to plagiarism.

• Talk to your students about academic integrity, broadly. Remind your students of the University’s Academic Dishonesty policy, raise questions to stimulate reflection: why is academic integrity valuable? Give examples.
  • See Academic Dishonesty and Student Cheating for additional guidance.
• Think about plagiarizing in the context of academic and professional value: if a task is easily performed by AI, does it need to be tweaked to continue fostering skills that are valuable on the job market?
• Shift from assessing products to assessing processes and student metacognition. 
• Flip the classroom to have students learn at home and produce in the classroom.
• Emphasize hands-on work in diverse media and connections to real-world experiences.
• Be on the lookout for AI-produced texts.
  • Currently, the University does not license or endorse any automatic AI detection tools, due to unreliable performance and bias concerns. 
  • Always consider student history and the broader context of the assignment before making a decision. When plagiarism is suspected, talking to the student individually is the easiest first step to addressing the problem.

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Writing Productive Prompts

Applying the principles and best practices of prompt engineering can help you get the most out of any AI tool. Generating useful and accurate output from Generative AI requires submitting requests and questions which properly prompt the artificial intelligence to produce your desired content. These tools are only as smart as what you ask of them, so consider the following when writing your prompts:

• Assign roles: For example, instead of asking “Provide restaurant recommendations for a trip to Italy,” try asking “I’d like you to act as a travel agent and provide breakfast and dinner recommendations for a 4-night trip.” Understand that in the context of academia, students could leverage this to prompt “I’d like you to act as a student and write a 4-page paper on the Titanic.”
• Be specific and responsive: Generative AI not only produces output to your initial prompt – it is responsive, as well. For example, prompting it to write a 4-page paper on the Titanic does not include sources and citations by default. However, responding to the initial paper with “Same paper with scholarly sources” will enhance the output with in-text citations and an APA-formatted reference list. You could even respond with “Same paper in MLA format” to switch it to a different citation style. This flexibility and adaptability is powerful for students who may misuse the tool to produce work on their behalf.

Relative to teaching and learning, consider these categories of Generative AI prompts:

Being familiar with the tool’s limitations also aids in recognizing content generated by it:

• Remember it is not a search engine: Some artificial intelligence tools (such as ChatGPT, Microsoft Copilot, and Google Gemini) can access search engines to incorporate more timely data into their output. Others (such as Claude), cannot and therefore are dependent upon their datasets. It’s important to consider if the output you seek would be impacted by the LLM’s dataset timestamp, and therefore, which tool is the best to use.
• Know the characteristics and limits of AI writing: While the style and tone of AI-produced material is seemingly accurate to human-produced writing on its surface, reviewing it through a more analytical lens quickly exposes its flaws. As an example, according to OpenAI’s documentation on ChatGPT, responses may be: 
  1. Factually inaccurate: Data set limitations can result in “inaccurate, untruthful, and otherwise misleading [output] at times” (OpenAI, 2024).
  2. Broad, and oversimplified: Responses are ”often excessively verbose and overuse certain phrases” (OpenAI, 2023).
  3. Biased: ChatGPT “may also occasionally produce harmful instructions or biased content” (OpenAI, 2024).

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