Wentworth’s statement on GenAI, Future Ready, asserts: “technology is evolving quickly, and education must keep pace to prepare students for future challenges and opportunities.” Wentworth aims to “produce graduates who are well-prepared to contribute to and lead in the rapidly changing landscape of technology and industry…as the demand for AI skills in the job market is rapidly increasing.”

Future Ready acknowledges that “AI raises important ethical and societal questions…such as bias and privacy.” Given that, it is important to strike a balance between embracing the possibilities of this new technology while guarding against harm.

Faculty at the Vanguard

Wentworth faculty are empowered to make teaching choices that work best in their disciplinary contexts and unique classrooms, labs, and studios. AI tool inquiries should be directed to the Information Security Office for an AI tool vendor risk assessment prior to starting the purchase process.  For software requests, please fill out the Software Acquisition Request Form. 

School-specific guidelines are in the process of being written, and various GenAI task forces and steering committees across Wentworth and the Colleges of the Fenway are working together to “harness the potential of AI to drive positive change for our academic community.”

Read more about Wentworth’s approach to GenAI in this article: Adoption of AI at Wentworth: Innovation, Ethics, and Education.

Pedagogy First

As with any technology tool, GenAI use should meaningfully align with student learning outcomes and intentionally support instructional goals.

Engage Critically

GenAI raises ethical, environmental, and data privacy concerns, which we recommend addressing with students. We encourage faculty to approach GenAI with a critical mindset, considering what will best support your students in their learning journeys.

Empower Students

Help your students develop critical thinking and media literacy skills. Check out the MLA’s Student Guide to AI Literacy, featuring skills-based learning objectives that you can use in your courses.

Try It Yourself

Before using GenAI in your teaching, we recommend that you explore the capabilities of different GenAI tools as you consider new avenues for integrating GenAI into your teaching practice.

Here are recommendations for critically engaging with GenAI tools:

• Research the vendor of a Gen AI tool you want to use. How is the company funded? Are there any ethical issues connected with the company? How do they store and use your input? Do they disclose what kinds of training data are used? What are their information security and data governance practices? Is the tool digitally accessible for all users?

• Do not enter personally identifying information (PII) or other sensitive information into a GenAI tool.

• Do not input others’ intellectual property into a GenAI tool without their explicit permission, such as student work. Many GenAI tools add your input to their training data without consent.

• Check all GenAI output for accuracy. Remember, its “intelligence” is artificial. Review and make any corrections before sharing GenAI-authored work with others, acknowledging the source. If you are using GenAI for research, double-check with secondary sources that GenAI-created material or summaries are accurate.

• Citation guidelines are emerging in different disciplines—check with your professional organization. But if you use AI-generated work, definitely make it clear to your audience that your work was created or partially created by a GenAI tool. Record the tool you used, the prompt you used, and the date and time. Learn more about citation guidelines in MLA and APA formats in this TurnItIn article or from the Chicago Manual of Style.

• If you decide to use GenAI in your classroom, consider offering ways for students to engage in the critical questioning outlined above before signing up for a particular tool. For example, if you want students to evaluate content generated by a GenAI tool in an introductory class activity, offer a pre-existing sample of output they can use instead of immediately using the tool themselves.

Develop GenAI course policies that best suit your student learning goals

This could range on a spectrum from: “GenAI is wholistically integrated into the course” to “GenAI is allowed on certain assignments in specified ways,” to “GenAI use is forbidden.”

Be aware that if your policy is to forbid GenAI use, you are relying on the honor system, as there is no definitive way to know if your students are engaging with GenAI tools.

Be transparent about why your GenAI course policies are in place

Invite students into a conversation about the process of developing your GenAI policies, making it clear how your policies will support their learning, and getting their buy-in to abide by your stated guidelines.

Specify which GenAI tools are allowed on assignments so that access to tools remains equitable, or collaboratively source GenAI tool suggestions from the class.

Make it clear how students will document their GenAI use. This could range from setting GenAI citation expectations to asking students to explain how they modified GenAI’s output to make the final product a true reflection of their uniquely human critical thinking and creativity.

Decide: AI resilient vs. AI embracing

Start by identifying an existing assessment in your course that could likely be done by GenAI. Put it to the test yourself by using a GenAI tool!

Consider why it is AI-vulnerable:

• perceived lack of value on the part of the student (make the purpose and relevance clear)
• cognitive offloading due to overwhelm (add scaffolding)
• AI is particularly good at the task itself (make the assessment multimodal)

In your redesign, address the underlying reasons for the AI vulnerability.

Then decide: do you want to rework it to be AI-resilient—harder for GenAI do to—or AI-embracing—meaningfully integrating GenAI and human capabilities?

AI resilient:

• Redesign the assessment to incorporate multiple stages, actionable feedback, and metacognitive reflection on the process.
• Require students to represent their ideas in multiple modalities, like textual (i.e. writing in diverse styles), visual (i.e. 2D/3D imagery, concept map, collage, meme) and/or auditory (i.e. voice memo, video, podcast).
• Include live, in-person elements like presentations, critiques, oral exams.

AI embracing:

• Reimagine the assessment as an authentic performance task that highlights how a professional would thoughtfully use GenAI in your field.
• Provide your students the opportunity to metacognitively reflect on the uniquely human process of creative problem solving as it intersects with the non-human capabilities of GenAI.
• Practice communicating their newfound skills as they would in a job interview.

Be equitable

• Reiterate your GenAI policies and expectations in your assignments, giving examples of proper GenAI use to help students understand the differences between thoughtful GenAI use and over-reliance on GenAI tools.
• Make it clear what is encouraged or acceptable use, and specify what approaches or crosses the line of academic dishonesty.
• Offer GenAI training and support to your students so they all have a shared knowledge base, providing them with a more equitable foundation for success.

Be inclusive

• Incorporate a mix of assessment approaches to support both inclusive teaching and GenAI resiliency.
• Consider offering assessment options across the spectrum from GenAI-forbidden to GenAI-embracing.
• Have conversations with your students about GenAI so you can better understand their perspectives and meet them where they are. After all, teaching and learning is a two-way street!

GenAI has opened up many new pathways to create content. This is challenging for academic disciplines that rely on indirect evidence to assess student learning.

What is Evidence of Learning?

An example of direct evidence is observing a student pipetting to see if they’ve met the learning objective of being able to “pipette with accuracy.” An example of indirect evidence is asking students to write a paper to demonstrate that they can “analyze a business case and provide evidence-based recommendations to a client.”

With indirect assessment, how can you know to what extent the student’s work is of their own creation? Unfortunately, there is no way to know with absolute certainty. For the business case example, students could use GenAI at many stages in the process. Some uses might be appropriate, while others could cross the line by inappropriately offloading their own cognition.

Transparency is key to academic honesty—students need clearly communicated expectations to stay within the boundaries that you set to support their learning, and they need to understand why those guardrails are in place.

Students will benefit from multiple ways to show what they know, and it will allow you to better assess the depth of their understanding. Start small by tweaking an existing assessment to make it an authentic performance task. In the business case example, consider other ways that students could demonstrate that they have met the learning objective.

For example: if students record themselves giving a 5-minute presentation with slides to an imagined client, it will likely provide more direct evidence of mastery of the learning objective than a potentially GenAI-aided written response. Additionally, it may take you less time to assess using a rubric, leaving you more time to give the student personalized feedback.

The False Promise of AI Detectors

As higher education GenAI thought leader Lance Eaton writes in his thoughtful series AI Plagiarism Considerations, “the AI detector is merely saying it certainly could be AI generated—but it has no actual evidence.”

Unfortunately, GenAI detectors rely on algorithmic probabilities that generate both false negatives and false positives. They are unreliable at best and inequitable at worst, and as this Stanford study asserts, they are more likely to indicate that the writing of English language learners is AI generated.

Also, for every self-proclaimed AI detection tool popping up, there is a counter-tool out there purporting to conceal AI-generated text (like this one). If students are motivated to cheat, there are a plethora of ways to outwit a tool’s abilities to detect it.

As Professor Susan Blum, the editor of Ungrading, writes: “If a student’s goal is simply getting a good grade, cheating is a reasonable strategy. But if their goal is learning, cheating is impossible.” Fostering intrinsic motivation and creating a culture of meaningful learning in your courses is ultimately a more effective way to promote academic honesty than attempting to police students in the brave new world of GenAI.

To learn more, check out this article from MIT: AI Detectors Don’t Work. Here’s What to Do Instead. For a deeper dive, explore this resource guide from GenAI thought leader Leon Furze: Rethinking Assessment for Generative Artificial Intelligence.

Approaching Academic Honesty Concerns with Students

Of course, you can trust your own intuition as an educator to wonder if something may be awry when you see a sudden, marked change in a student’s quality of work. If you believe that a student may be using GenAI inappropriately, or have other academic honesty concerns, here are some steps to take:

• Don’t take it personally

• Engage the student in a private one-on-one conversation

• Open the dialogue with curiosity and compassion

• Share your concerns without blame or judgment, leading with observations, not accusations (“I noticed that your writing style in this paper is different from the in-class writing sample you did a couple of weeks ago in these specific ways…tell me more about your writing process at home…”)

• Consult with faculty colleagues and/or TLC instructional designers to brainstorm new ways to approach the situation

• If you feel the need to escalate the situation, speak with your Dean

Wentworth’s academic honesty policies exist to support genuine student learning. Certainly refer to these policies as you set the boundaries for GenAI use that best support student learning in your courses.

TLC helps faculty design transformational learning experiences

Instructional designers are educators with disciplinary expertise in the art and science of teaching and learning. Faculty bring subject matter expertise, and we partner with creative educational forms to help bring your content to life.

As our campus’s Center for Teaching & Learning, TLC is Wentworth’s internal educational consulting group. We serve as thought partners to help you:

• Reimagine your courses to be more future ready, crafting learning objectives that align with developing industry needs and GenAI use cases

• Redesign your assignments and assessments to meaningfully integrate GenAI

• Redevelop your assignments and assessments to make them more GenAI resiliant

• Create engaging lesson plans involving GenAI

• Develop strategies for addressing student challenges and engaging students in productive conversations about GenAI

• Craft a GenAI policy that supports academic integrity and learning in your unique courses

• Assess the efficacy of your GenAI-related teaching interventions

• Observe your course to give you supportive and actionable feedback on your GenAI teaching initiatives

• Conduct research related to your GenAI-related teaching innovations

Reach out to us at teach@wit.edu, and instructional designer will be happy to partner with you.

We hope these GenAI resources inspire you with new ideas and teaching strategies.

GenAI Tools

• This Massachusetts Library System Guide links out to a plethora of GenAI tools for a variety of purposes.
• This introduction to the GenAI ecosystem is by José Antonio Bowen, co-author of Teaching with AI: A Practical Guide to a New Era of Human Learning.

Teaching Approaches

Below are high-level resource guides about teaching with GenAI with many links for deeper diving from the following sources:

• US Department of Education
• Harvard University’s AI Pedagogy Project
• OpenAI, the company behind ChatGPT

Teaching Strategies

Below are guides to teaching with GenAI featuring practical teaching strategies from the following sources:

• Massachusetts Institute of Technology’s AI Resource Hub
• University of Maine’s Learn with Al Toolkit
• Professors at Play’s AI Playbook

Critical Perspectives

GenAI tools can reproduce cultural stereotypes and biases in both text and image generation, and have significant environmental impact. Check out the resources below for thoughtful, research-driven discussions about GenAI.

• To explore the ethics of GenAI, read the Organisation for Economic Co-operation and Development’s report Ethical Framework for AI in Education, or watch videos from the course AI Ethics: Global Perspectives.
• From the International Journal of Educational Technology in Higher Education: Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives
• From Higher Education Research & Development: A scholarly dialogue: writing scholarship, authorship, academic integrity and the challenge of AI

AI Literacy Frameworks in Education

AI literacy frameworks are vital resources that help individuals develop the knowledge and skills necessary to thrive in a world increasingly influenced by AI, especially in higher education contexts. These frameworks are designed to promote critical thinking, ethical understanding, and hands-on competence with AI technologies.

• Digital Promise/Educause Framework
• AI Literacy in Teaching and Learning: A Durable Framework for Higher Education
• AI Literacy: An Opportunity for Higher Education