Inquisitive Attitude
An inquisitive attitude means you approach your own development the way a researcher approaches a problem: with curiosity about what is working, a willingness to question your own assumptions and a habit of reframing setbacks as information rather than failure. This disposition is often the hardest part of learning to measure and teach, yet it is what drives growth.
For an ICT student, it also shapes how you use AI in your learning process. Critical thinking and proactivity are essential here: AI can support your thinking, but it won't build the underlying disposition for you if you rely on it passively.
Starting Points
- Limeri et al. (2020), "Growing a growth mindset: characterizing how and why undergraduate students' mindsets change" Shows you, through the words of other STEM students, how struggle and setbacks shape whether you see your abilities as fixed or improvable, so you can recognise and interrupt that pattern in yourself.
- Bushra & Bushra (2024), "Epistemic Curiosity and Academic Self-Regulatory Learning in Undergraduates" Shows you, based on a study of undergraduates your age, how curiosity about a subject builds your confidence and your ability to plan and steer your own learning.
- Kosmyna et al. (2025), "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task" Confronts you with evidence that outsourcing thinking to AI can weaken your own engagement and memory, giving you a concrete reason to stay questioning and deliberate about when and how you use it.
Key Points
- You regularly pause to name what went well and what could be better in your own way of working, reframing setbacks as information about your approach rather than proof of a fixed limit, and you can say concretely what you learned from it.
- You actively ask for feedback from more than one source (a teacher, a peer, a client, or your own retrospective notes) and turn it into a concrete next action or an adjusted learning goal, rather than letting it sit unused.
- You make deliberate choices about what you learn, how, and with whom — including when and how you use AI — and you question an AI's output on your own learning or work instead of accepting it uncritically.
- You notice things you've figured out or struggled with and proactively share them with teammates or classmates, even when nobody asked you to.