Conference Program

In a world increasingly shaped by artificial intelligence and rapid technological transformation, education must prepare students not only to use digital tools, but also to critically understand and evaluate the technoscientific systems that structure contemporary life. The ubiquity of AI—especially among younger generations—raises urgent pedagogical questions: how can schools integrate these technologies without reinforcing passive consumption, dependency on automated outputs, or technocratic reductionism? How can AI instead strengthen democratic agency and responsible participation, rather than functioning as an efficiency device that short-circuits critical thinking?

Building on its educational proposal, this contribution outlines a co-design framework integrating generative AI, playful learning, and reverse thinking to foster critical AI literacy, creativity, and civic empowerment. Reverse thinking operates as a methodological lever that challenges the expectation that technology should primarily deliver fast, correct answers. In this approach, AI is repositioned as a dialogic partner that supports inquiry, encourages hypothesis-making, and generates productive uncertainty. The aim is to cultivate learners who can question AI outputs, identify assumptions, compare perspectives, and negotiate meaning—competences essential for navigating AI-saturated environments and for engaging in democratic debates about technology and governance.

The project translates conventional curricular topics into interactive game-based experiences. Teachers and students collaboratively transform disciplinary content into logic-based challenges, narrative scenarios, or rule-driven problems, while AI supports ideation, iteration, and critical testing. Prompt engineering becomes not merely technical training but a reflective practice: learners learn to formulate better questions, explore system behavior, test alternative framings, and validate claims. Teachers, in turn, act as designers, facilitators, and “game masters,” guiding exploratory pathways where progress depends on reasoning, collaboration, and informed decision-making rather than on receiving immediate solutions.

Crucially, the framework situates learning within emotionally meaningful experiences. Complex systems—technological, ecological, economic, or social—are often perceived as unstable and difficult to control, potentially triggering anxiety, stress, and disengagement, particularly in performance-oriented school cultures. AI-mediated playful learning offers protected environments where uncertainty, error, negotiation, and redesign are normalized. Through gameplay, learners experience tension, curiosity, surprise, and satisfaction in manageable forms, gradually developing emotional regulation and resilience. Mistakes become generative moments that spark exploration rather than fear; complexity becomes a space for collective problem-solving rather than a source of toxic stress. Positive emotions thus function as a pedagogical infrastructure supporting deep learning, sustained attention, and persistence in challenging tasks.

Play becomes a laboratory for navigating complexity, where technological awareness grows alongside metacognition, ethical reflection, and collective responsibility. By designing and discussing rules, evaluating AI-generated options, and reflecting on consequences, students practice civic reasoning and learn that technoscientific systems are not neutral but shaped by values, trade-offs, and power relations. Used critically, generative AI can help surface these dimensions by offering alternative perspectives, simulating outcomes, and prompting discussion about bias, reliability, and accountability.

Overall, the proposal contributes to debates on science, technology, and democracy by showing how AI-supported playful learning can cultivate not only digital competence, but also ethical awareness, epistemic empowerment, and the capacity to inhabit contemporary complex systems with agency and well-being.