Higher education is at inflection point. Traditional lectures no longer reach students as they once did, and learners are disengaging now that AI can seemingly do the work for them. Yet our approaches to motivating students remain rooted in performance metrics and delineated schedules or deadlines.
But this is not a crisis; it’s an opportunity for redesign. Using what we know as educators and experts, how do we design learning environments in which students want to do more than just complete assignments?
Across recent projects, I’ve collaborated with colleagues on initiatives looking at what students need to engage more deeply with their learning. These efforts vary in scope and subject, but all – whether it’s training teaching assistants (TAs) or integrating AI – show that their sum is greater than the parts, and each collaborator acts as a multiplier.
The key role of teaching assistants in motivational design
One promising initiative in motivating students lies in how we train and empower TAs. They are often frequent educators in large courses, especially in STEM disciplines. Their interactions with students set the tone for engagement, trust and persistence. And yet TA training, when it does occur, often focuses on administrative policies and logistics, with little attention given to the interaction, scaffolding and feedback that drive student motivation.
That’s the gap we’re addressing with our computer science TA training workshops and Vista (virtual interactive simulation for teaching assistants), a collaborative project developed with motivation expert Brett Jones and user experience expert Denis Gracanin. Vista uses extended reality (XR) and AI-driven scenarios to help TAs practise office hour sessions – especially those that influence how students perceive autonomy, belonging and competence.
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TAs learn strategies to help students solve problems independently, guiding them without giving them the answer. Understanding motivation along with practice sessions can remind TAs to show students they care about them and their learning, whether students are enthusiastic or experiencing frustration in the face of a challenge.
What we’ve found is that when TAs are prepared to use a caring manner and strategies to help students become more self-regulated and believe in their ability to succeed (and not just deliver information), both groups benefit. Students feel more capable and supported, and TAs themselves gain a stronger sense of belonging in computer science.
This dual effect suggests that faculty investment in TA development and support can reshape the motivational climate across entire departments.
Demystifying AI for effective use
Alongside rethinking TA training, we’re also redesigning how students interact with AI. Rather than treating AI tools as threats or novelties, we position them as opportunities to advance skills and deepen reflection while maintaining ownership.
In one sophomore-level computer science course, revised in collaboration with AI expert Naren Ramakrishnan and Sehrish Basir Nizamani, we guide students to explore how to use large language models (LLMs) thoughtfully and ethically, while also showing them how the models work and exposing what is inside the “black box”. This helps demystify their power while also showing their limitations. Then, students solve real-world practical challenges where AI can assist, but not replace, human judgement.
The emphasis isn’t on policing AI use but on teaching students to ask better questions: why did this prompt work? Why wasn’t that one effective? How do I validate the output? Is my approach the right one? In other words, we make space for reflection. Metacognition – thinking about thinking – is a key driver of motivation.
Students also find the usefulness and relevance of LLMs highly motivating. By treating AI with transparency, and – importantly – as a partner (not a shortcut) in problem-solving, we help students develop a sense of agency. That shift alone can change the trajectory of their engagement.
A note on research and belonging
Alongside curriculum revisions and TA training, it’s worth noting that research experiences also offer rich opportunities for student engagement. Our Broadening Undergraduate Research Groups in Systems programme encourages students who might otherwise miss out on opportunities for meaningful, leading-edge research through structured mentorship. Such experiential learning demonstrates that when students are treated as contributors – not just consumers – motivation flourishes.
Leveraging faculty expertise to build meaning
Across these efforts, one theme emerges: faculty are not just knowledge experts – we are motivational designers. We have the power to shape how students connect with the work, with each other and with themselves. Students will always have potential shortcuts, but they will still turn to us – faculty and TAs – to help them find meaning. As educators, we can design our courses, assignments, projects, content-delivery and interactions to reframe the question from: “How do I get students to do the work?” to “How do I make the work worth doing?”
The most powerful motivator we can offer our students isn’t a grade, a threat or a chatbot. It’s the sense that what they’re learning is connected to real problems, real people and real purpose.
Margaret Ellis is professor of practice in the department of computer science at Virginia Tech. She received the 2024 IEEE Computer Society Mary Kenneth Keller Computer Science & Engineering Undergraduate Teaching Award in recognition of outstanding contributions to undergraduate education.
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