AI as Your Toughest Teammate: Dr. Job Fransen on Sparking Critical Thinking in Sport Science
How an adversarial AI collaborator is transforming student learning in the subject growth, motor development and ageing
Dr. Job Fransen teaches EHR 225: Growth, Motor Development and Ageing at Charles Sturt University. In this interview, Fransen discusses how AI is being used in EHR 225: Growth, Motor Development and Ageing.
Source note: This is an edited interview adapted from a narrated video submitted to OpenAI. Watch the associated video in OpenAI Academy.
Intro
What happens when you invite artificial intelligence to play devil’s advocate in the classroom? Dr. Job Fransen, associate professor at Charles Sturt University and a leading voice in skill acquisition, has done just that. In his course, EHR 225: Growth, Motor Development and Ageing, Dr. Fransen uses AI not as a shortcut, but as a challenger—pushing students to defend, rethink, and deepen their understanding during video-based assessments. In this article, Dr. Fransen shares the theory behind this approach, the practicalities of implementation, and the impact he’s seeing on student learning.
The Interview
Q: Dr. Fransen, you describe AI as an “adversarial collaborator” in your classroom. What does that look like in practice?
Fransen: The core idea is to position AI as a kind of sparring partner for students. In our video-based assessments, students analyze a common issue in youth sports called the maturity bias, where more mature youth athletes are given better development opportunities than those athletes who have their adolescent growth spurt at a later time . The students develop arguments in favour, or against, specific methods that have been reported in the scientific literature to address the maturity bias. They then submit their arguments to an AI partner, who intentionally challenges their reasoning. It might point out overlooked variables, question their assumptions, or suggest alternative interpretations. This forces students to revisit their arguments, defend their choices, or sometimes even change their minds, which are all important in critical reflection.
Q: Why take this adversarial approach, rather than using AI as a supportive tutor?
Fransen: Supportive AI is valuable, but I wanted to go a step further. In sport and exercise science, and especially in skill acquisition, critical thinking is essential. Our field is dominated by opinions. Think for example about how often you’ve been given contrasting exercise advice in your life. We’re not looking for rote answers—we want students to grapple with complexity. By making AI a constructive adversary, students are exposed to the kind of rigorous debate they’ll encounter in professional practice. It’s about building intellectual resilience.
Q: Can you give an example of how this plays out with your students?
Fransen: Absolutely. For instance, a student might argue in favour of grouping athletes by their maturity status (i.e. all the earlier maturing athletes compete with and against one another) rather than the traditional chronological age groupings based on athletes’ birthdates. . The AI might counter: “Have you considered that physical and psychological maturity are not the same thing? An athlete may be physical mature, but lack psychological maturity which could make grouping based on physical maturity harmful.?” The student is then required to revisit the literature, reconsider their analysis, and respond thoughtfully. It’s a dynamic, iterative process—much closer to real-world problem-solving.
Q: What theoretical foundations inform this approach?
Fransen: I draw heavily on constructivist theories of learning, which emphasize that knowledge is built through active engagement and challenge. The adversarial AI acts as a catalyst for cognitive conflict, prompting students to question their assumptions and integrate new perspectives. In skill acquisition research, we know that learning is most robust when it’s effortful and dialogic. At the same time, this method of adversarial collaboration mimics a method commonly used to resolve scientific disagreements, which plays well into the scientific underpinning of our degree.
Q: What’s been the impact so far?
Fransen: The results have been very promising. Students report that the AI’s challenges push them to think more deeply and creatively. We’re seeing higher-quality analyses, more nuanced arguments, and a greater willingness to engage with uncertainty. It’s not always comfortable—but that’s the point. The discomfort leads to growth. At the same time, we are also seeing many students interact with AI for the first time. Our cohorts consist of many students who are retraining after a partial career in a different field. These students are often less familiar with how to use AI appropriately.
What Stands Out
Core idea: AI is used as an adversarial collaborator to challenge students’ reasoning and deepen critical thinking.
Classroom design: Students submit video-based analyses that AI then critiques by questioning assumptions and suggesting alternative perspectives.
Student impact: Students develop more nuanced arguments, engage more deeply with complexity, and show greater intellectual resilience.
Transferable lesson: Positioning AI as a constructive challenger can foster effortful, dialogic learning aligned with real-world professional practice.
Bio
Dr. Job Fransen Job Fransen is an associate professor in Sport and Exercise Science at Charles Sturt University and a renowned skill acquisition researcher. He is known for his innovative, proactive and creative approach to teaching and learning.
This resonates with what I’m building in my own AI-native capstone in academia. Fransen’s “adversarial collaborator” framing names something I’ve found essential: the value isn’t in AI smoothing the path but in roughening it productively. Cognitive conflict is the engine, and a too-helpful tutor quietly removes the friction that makes learning stick.
What strikes me most is the alignment between the pedagogy and the epistemology of the field itself. By having AI mimic the adversarial collaboration scientists use to resolve disagreements, students aren’t just learning content—they’re rehearsing the disposition of the discipline. That’s a subtle but profound design choice. The method teaches the same intellectual virtue it demands.
The detail about retraining students encountering AI for the first time is also worth dwelling on. There’s a real risk that “adversarial” reads as “discouraging” to a newcomer. I’d be curious how Fransen scaffolds the early encounters so the discomfort lands as challenge rather than defeat—because intellectual resilience has to be built, not assumed.
Either way, a constructive challenger that forces you to defend your reasoning is far closer to professional reality than a system that just hands you an answer. Thanks for sharing this.