Empowering Minds in the Age of AI

15 September 2025
Huiwen Wang et al.
empowering minds in the age of ai poster with boy in glasses lifting weights over his head

 

 

Empowering Minds in the Age of AI: 

The ACE Framework for Cognitive Immunity and Pedagogical Intervention

 

Huiwen Wang1, Huan Yang2, Yang Wu3 

 

1 Canterbury Christ Church University / Communication University of China, Nanjing
2 Communication University of China, Nanjing
3 Nanjing Institute of Industry Technology

 

Artificial Intelligence (AI) has rapidly transformed the landscape of higher education, reshaping how knowledge is accessed, produced, and evaluated. Generative AI systems such as ChatGPT, Midjourney, and other large-scale models now play a central role in students’ learning environments. While these technologies create unprecedented opportunities for personalized feedback, multimodal learning, and creative exploration, they also pose new risks. Students may become dependent on automated outputs, fall into the trap of “automation bias,” or develop an illusion of understanding without genuine critical engagement. These challenges raise pressing questions: how can educators ensure that students remain active, reflective, and critical learners in an age where AI can both amplify and undermine human cognition? 

Our poster introduces the ACE Framework—a triadic model integrating AI, Cognition, and Education—as both a diagnostic and pedagogical tool to address these challenges. The framework is built on the concept of cognitive immunity, defined as the capacity of learners to critically evaluate, filter, and adapt to AI-generated content without surrendering their agency. Drawing inspiration from cognitive science, metacognition research, and pedagogical theory, the ACE Framework provides a systematic pathway for fostering resilient learners who can harness AI’s affordances while guarding against its cognitive risks. 

Theoretical Foundations 

The ACE Framework is anchored in four key strands of theory: 

  1. Constructivism – Learning is an active process of meaning-making rather than passive reception. When students engage with AI outputs, they must move beyond consumption toward construction, comparing, questioning, and re-shaping knowledge. 

  1. Metacognition and Flavell’s Monitoring Model – Effective learning requires awareness of one’s thinking processes. The ACE Framework embeds explicit strategies for students to monitor, plan, and adjust their interactions with AI tools. 

  1. Community of Inquiry (CoI) – Social presence, teaching presence, and cognitive presence remain vital in blended and AI-mediated contexts. Educators must create learning environments where AI complements rather than replaces human dialogue and reflection. 

  1. Cognitive Immunology and Risk Research – Borrowing the metaphor of immunity, the framework views cognitive risks (e.g., over-reliance, superficiality, misinformation) as “infections” that can be countered through preventive and corrective educational interventions. 

The Three Pillars of the ACE Framework 

  1. AI as a Cognitive Partner 

AI is positioned not as a replacement for human reasoning but as a scaffold for exploration. Students are encouraged to interrogate AI outputs, identify inconsistencies, and integrate them with disciplinary knowledge. This requires explicit instruction in AI literacy, including prompt design, verification, and cross-referencing strategies. 

  1. Cognition as Reflexive Engagement 

Students’ critical thinking and metacognitive skills are foregrounded through the Explanation Depth Index (EDI), an innovative tool developed in this research. The EDI measures how deeply students engage with AI-mediated explanations, distinguishing between surface repetition and reflective integration. By quantifying depth of explanation, educators gain actionable insights into learners’ cognitive engagement. 

  1. Education as Pedagogical Intervention 

Pedagogy becomes the mediating force that aligns AI and cognition. Educators deploy structured interventions—such as guided reflection prompts, collaborative critique, and adaptive feedback loops—that both leverage AI’s generative power and safeguard against its cognitive pitfalls. Teaching is reframed as creating conditions for critical dialogue between humans and machines. 

Practical Application: The Learning Reflection Assistant 

As part of this project, a prototype Learning Reflection Assistant has been designed. This system integrates AI models with a database of metacognitive prompts to support the awareness–planning–monitoring–adjusting cycle of learning. For instance, when a student generates an AI-based draft, the assistant intervenes with prompts such as: 

  • What assumptions underlie this explanation? 

  • Can you identify gaps or contradictions? 

  • How might you reframe this in your own words? 

Such scaffolding ensures that students practice not only knowledge acquisition but also reflective judgement. 

Empirical and Educational Significance 

Initial pilot studies in Chinese universities suggest that the ACE Framework can be embedded within general education AI literacy courses. Students who engaged with the EDI-based reflective tasks demonstrated greater awareness of their reliance on AI, improved their ability to identify superficial explanations, and reported higher confidence in independent problem-solving. These findings highlight the potential for the ACE Framework to serve as both a research lens and a teaching tool in diverse educational contexts. 

Contributions and Implications 

The ACE Framework contributes to ongoing debates in three major ways: 

  1. Conceptual Integration – It bridges AI ethics, cognitive psychology, and pedagogy, offering a holistic lens on how learners can thrive in AI-mediated contexts. 

  1. Diagnostic Innovation – The Explanation Depth Index provides a measurable, replicable method for evaluating reflective engagement with AI content. 

  1. Practical Pedagogy – It offers educators a set of actionable strategies, moving beyond abstract concerns to classroom-ready interventions. 

Looking forward, the framework holds implications for curriculum design, teacher training, and policy development. Universities can embed ACE-based approaches into AI literacy programs, while policymakers can use cognitive immunity as a guiding principle for responsible AI education. 

Conclusion 

The rise of AI presents both opportunities and challenges for higher education. While AI can extend human cognitive capacities, it can also mask superficial learning and foster dependency. By equipping learners with cognitive immunity through the ACE Framework, educators can empower students to remain critical, reflective, and resilient in their engagement with AI. This presentation calls for a pedagogical shift: one that recognizes AI as an inevitable partner but insists on maintaining human agency at the centre of learning. 

Ultimately, empowering minds in the age of AI is not merely about adapting to new technologies. It is about cultivating learners who can think critically, act responsibly, and engage reflectively in a world where human and machine intelligence continually intersect. 

View the full poster here: 

Wang, H. (2025). Empowering Minds in the Age of AI: The ACE Framework for Cognitive Immunity and Pedagogical Intervention. AIEOU Inaugural Convening 2025, University of Oxford. Zenodo. https://doi.org/10.5281/zenodo.17186766 

 

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