The Value of Cognitive Dissonance
Artificial intelligence has fundamentally altered how we access information, create content, and solve problems. AI can quickly answer increasingly complex questions. For educators and learners alike, this unprecedented access to on-demand knowledge offers tremendous opportunities for long-term cognitive growth. However, amid the excitement surrounding AI’s transformational potential, one educational principle remains vital: meaningful learning requires productive struggle.
The purpose of education has always been to develop thinkers. As AI becomes more proficient at generating realistic solutions rather than hallucinations, educators must resist the impulse to disregard cognitive processes that foster deeper understanding. The challenge then becomes redesigning learning experiences that leverage AI’s potential while preserving the intellectual effort necessary for growth.
The Value of Cognitive Dissonance
Psychologist Leon Festinger’s (1957) theory of cognitive dissonance offers an important foundation for understanding why cognitive struggle matters for transformational development. Festinger contended that when individuals encounter information that conflicts with their existing beliefs or understanding, they experience some level of psychological discomfort and disassociation. This discomfort motivates them to navigate through inconsistencies by critically examining evidence and reconsidering assumptions, while constructing new knowledge pathways.
In education, these moments of disequilibrium and imbalance are often where authentic learning begins. If AI is used to resolve every uncertainty before learners wrestle with competing and inconsistent ideas, opportunities for true cognitive transformation diminish. Learners may receive correct answers without ever tackling the misconceptions that originally challenged and limited their understanding. Learning may become increasingly more efficient but potentially less transformative.
Educators should encourage learners to first create predictions, justify their reasoning, identify gaps in understanding, and then use AI as a collaborative thought partner rather than an intellectual substitute.
Transformation Requires Reflection
Jack Mezirow’s theory of transformative learning suggests that meaningful learning develops through critical reflection rather than passive information acquisition. According to Mezirow (1991), authentic, transformative learning occurs when learners analytically examine their assumptions, question recognized perspectives, and reconstruct their understanding through reflective discourse.
AI can support this process by creating alternative viewpoints, challenging arguments, or posing reflective questions. However, the transformation itself remains a profoundly human endeavor.
However, deeper reflection cannot be unilaterally outsourced.
The learner must still be cognitively active in evaluating evidence, reconciling competing perspectives, and determining how new information reshapes existing beliefs. AI may facilitate reflection, but it cannot pass through transformation on behalf of the learner.
Educational experiences should therefore prioritize reflection before automation. AI becomes most valuable after learners have engaged in careful analysis and self-examination.
Deeper Understanding Is an Interpretive Process
Philosopher Hans-Georg Gadamer stated that awareness is never just the accumulation of information. In Truth and Method, Gadamer (1975/2004) suggested that perception emerges through interpretation: a dynamic “fusion of horizons” in which learners integrate prior experiences with new encounters to create meaning.
This insight has significant implications for AI-enhanced education.
While Gen-AI is adept at creating information, information alone does not equate with deeper understanding and mean-making. Learners must still interpret, contextualize, evaluate, and integrate ideas within their own lived experiences. No algorithm can fully replace the interpretive dialogue through which understanding develops.
Thus, transformational learning remains fundamentally hermeneutic.
Every interface with AI should become an opportunity for learners to ask deeper questions:
• Why is this explanation persuasive?
• What assumptions underlie this response?
• How does this compare with my own understanding?
• What perspectives may be missing?
These questions transform AI from just an answer generator into a catalyst for greater interpretation.
Productive Struggle Builds Expertise
Research across educational psychology demonstrates that cognitive disruptions strengthen long-term learning. Although immediate executive functioning may appear slower, learners who actively retrieve information, solve challenging problems, and reflect on errors are shown to develop stronger conceptual understanding and greater knowledge transfer.
If AI completes every cognitively demanding task, learners may experience an illusion of competence. While work appears complete, understanding remains superficial. Learners become increasingly dependent on technological assistance rather than developing the expertise necessary for independent reasoning.
The goal should never be to remove cognitive challenges.
The goal is to design such challenges that AI helps elucidate rather than eliminate.
The Future Belongs to Thoughtful Learners
AI will continue to become more integrated into our educational systems. Yet the qualities that define meaningful and transformational learning—curiosity, judgment, reflection, interpretation, and wisdom—remain uniquely human capacities.
Productive struggle is not an obstacle to learning. Rather, it is the cognitive mechanism through which learning occurs.
Thus, educators are tasked not with competing with AI but with cultivating the human intellectual habits that AI cannot replace. When learners engage with uncertainty, wrestle with competing ideas, reflect critically on their assumptions, and construct meaning through interpretation, they develop capacities that no single technology can replace.
The future of distance learning will not be defined by how quickly AI is integrated as a learning partner into existing educational spaces.
It will be defined by how intentionally educators design experiences that require learners to think before they prompt, reflect before they automate, and understand before they trust.
In an AI world, productive struggle is not becoming obsolete. It is becoming indispensable to the authentic human learning experience.
References
Festinger, L. (1957). A theory of cognitive dissonance. Stanford University Press.
Gadamer, H.-G. (2004). Truth and method (2nd rev. ed., J. Weinsheimer & D. G. Marshall, Trans.). Continuum. (Original work published 1975)
Mezirow, J. (1991). Transformative dimensions of adult learning. Jossey-Bass.
Rebecca Blankenship
Rebecca J. Blankenship is an award-winning educator and researcher with over 25 years of teaching experience. Her current research examines the ecologies of meanings as a systems-based, hermeneutic approach to ethics in AI and gen-AI teaching and learning modalities. She is currently an Associate Professor in the College of Education at Florida Agricultural and Mechanical University.