Generative AI is rapidly changing higher education, but how can universities ensure it enhances learning rather than replacing the skills students need to develop?

In our recent webinar, AI in Higher Ed: The Conversation Every University Should Hear, experts from higher education explored some of the biggest questions facing institutions, faculty and students from AI literacy and academic integrity to human skills, faculty involvement and equity.

Watch the full webinar to hear the conversation and perspectives from: 
 

Following the session, we asked our speakers to answer some of the questions we didn't have time to cover during the live Q&A. We will be adding the responses as and when, so please come back to this article to see these updates. 

Q&A with the speaker panel

Response from Nurun Nahar:  "Transparency in communication about GenAI use is essential. If students see staff members setting examples of good practice of using GenAI responsibly be it for preparing teaching and learning resources or in assessments and feedback, it promotes accountability in both parties which is a professional integrity skill that sits well with employability. I would encourage constructive discussion about AI (ensuring staff are familiar with how GenAI tools function and have the technical knowledge) as it helps students to understand where these technologies could add value in their learning, research or professional skills development. I think one of the biggest faculty training to focus on is to help staff with pedagogic knowledge development alongside development of digital and AI skills. If staff feel confident with their pedagogic knowledge, their digital capabilities, knowledge about contents being delivered and technical knowledge, they will have better means to support students with their learning (learning how to learn). Training face to face and online have their own pros and cons and is resource dependent but mixed modality works best when staff are stretched for time during busy academic periods." 

Response from Nurun Nahar: There are some strong evidences (e.g. OECD, WEF) that emphasises on developing meta skills alongside technical skills as those skills are essential for lifelong learning and would support learners to adapt to changing technical and workplace environments. These can be developed through learning experiences designed around authentic formative and graded assessments.

Response from Patricia Baia: In higher education, I actually don't see these as competing goals. I think good AI-integrated teaching should make our human skills more important, not less. For example, in law, AI can create a negotiation scenario or simulate a client. But the student still has to negotiate. They still have to listen, read the room, build trust, communicate, make ethical decisions, and exercise judgment. Those are skills AI can't simply replace. So I think we need to intentionally design learning experiences that require students to practice communication, collaboration, negotiation, empathy, active listening, persuasion, leadership, ethical reasoning, and judgment. To me, it's not human skills versus AI skills. It's about developing both. We want students who are digitally and AI literate, but who are also really good at being human.

Response from Nurun Nahar: Decision about use of AI tools should be considered taking into account and in consultation with those who will be directly impacted as a result of that decision. Currently there is a gap in honest consultation using relevant methods but there are evidences emerging of how institutions are involving students and staff in making certain decisions about procurement of tools but very sparse. It is the job of senior executives and those with the power to close that gap. Having the right leadership in place is important and more importantly having leaders who are equipped with the knowledge, skills and experience fit for the task.

Response from Patricia Baia: I think there is definitely a gap, and sometimes we underestimate how frustrating that can be for faculty. Faculty understandably want control over what happens in their classrooms. That's part of academic freedom and good pedagogy. At the same time, the institution has responsibilities around privacy, security, accessibility, student data, procurement, and compliance. So I don't think the answer is for faculty to decide everything, and I don't think the answer is for administration to decide everything either. I think this really needs to be a shared conversation. Faculty should have a strong voice in how AI is used for teaching and learning, while the institution provides the guardrails around things like privacy, security, accessibility, and approved technologies. Ultimately, faculty shouldn't feel that AI is something being done to them. They should feel that they have a voice in how it is being used to improve teaching and learning.
 

QUESTION: Are you concerned about staff or students becoming addicted to AI tools or becoming overly reliant on them? How do we promote AI while being cognizant of these issues?

Response from Patricia Baia: Yes, I think it is something we need to be mindful of, but I’m probably less concerned about the word “addiction” than I am about students becoming overly dependent on AI and letting it do the thinking for them. My biggest concern is when students start outsourcing the very skills we are trying to develop (i.e., writing, analyzing, problem-solving, and making judgments). At Touro, I talk about AI as a tool to support thinking, not replace thinking. In addition, I use the term AI Sandwich to talk about making sure the Human is included in the results. Use AI strategically, and then come back to your own work to question it, evaluate it, revise it, and make the final judgment. For me, the goal isn't to discourage AI use. It's to teach students and faculty how to use it without losing the ability to think for themselves.

QUESTION: What are your top tips or GenAI guides/frameworks for students and faculty?

Response from Patricia Baia: My biggest advice is to keep the guidance simple. If our AI policies and guidelines are so complicated that nobody reads them, they aren't very helpful. For faculty specifically, I like to start with five questions: What are students supposed to learn? Why would AI help them learn it? What are they actually allowed to do with AI? What will demonstrate that they learned it? And how will they show their own thinking? That shifts the conversation from “Can students use AI?” to a much more useful question: “What is the best way for AI to support this particular learning objective?”

QUESTION: How big is the gap between how much say faculty want over AI tools and how much they actually have—and whose job is it to close it?

Response from Patricia Baia: I think there is definitely a gap, and sometimes we underestimate how frustrating that can be for faculty. Faculty understandably want control over what happens in their classrooms. That's part of academic freedom and good pedagogy. At the same time, the institution has responsibilities around privacy, security, accessibility, student data, procurement, and compliance. So I don't think the answer is for faculty to decide everything, and I don't think the answer is for administration to decide everything either. I think this really needs to be a shared conversation. Faculty should have a strong voice in how AI is used for teaching and learning, while the institution provides the guardrails around things like privacy, security, accessibility, and approved technologies. Ultimately, faculty shouldn't feel that AI is something being done to them. They should feel that they have a voice in how it is being used to improve teaching and learning.

QUESTION: Could AI increase the gap between rich and poor in skills, knowledge, and resources?

Response from Patricia Baia: Absolutely. I think equity has to be part of the AI conversation from the beginning, not something we address after the technology is already in place.  In addition to access, we are starting to see an AI divide around who knows how to use these tools well. Some students have access to the newest models, better technology, paid subscriptions, and people who can teach them how to use AI effectively. Other students may have limited access or may be using AI without really understanding its limitations. Universities have an important responsibility here. We need to provide access, but access alone isn't enough. We also need to provide AI literacy and guidance. Otherwise, there's a real risk that AI will amplify the inequities that already exist in higher education rather than help close them.