Have you ever found yourself reaching for an artificial intelligence (AI) tool like ChatGPT or Gemini before even attempting to solve a problem on your own? If so, you’re not alone. This impulse is more than just a modern convenience; it’s rooted in the way our brains are wired to seek instant gratification. Just as “mindless scrolling” on social media can become habitual (Kazmi et al., 2025), so too can “mindless information seeking” through AI. The allure of immediate answers is powerful, and as educators, we are witnessing a growing trend toward students’ increased reliance on AI for quick solutions, often at the expense of their own critical thinking (Gerlich, 2025).
This article explores how the brain’s reward system, particularly the role of dopamine, may be driving this shift, and what educators can do to address it while still harnessing AI’s benefits in the learning experience.
Understanding Dopamine’s Role in AI Use
Dopamine, a key neurotransmitter in the brain, plays a central role in motivation, learning, mood, and reinforcing rewarding behaviors. When we anticipate or receive something rewarding, such as a correct answer or a “like” on social media, the brain’s reward center is activated, signaling that the experience was valuable and worth repeating. This reward pathway, modulated by the release of dopamine, motivates us to seek out similar experiences in the future. For instance, AI can positively engage this process when it is used creatively to deepen thinking, sparking excitement and generating new ideas. However, dopamine’s influence is not limited to positive outcomes. Over-reliance on AI may also lead users to “cut corners,” prioritizing quick answers over independent critical thinking. Ultimately, dopamine’s ability to respond to unexpected or novel events helps us learn from both rewards and mistakes.
Repeated engagement in rewarding behaviors, such as quickly finding answers with the use of AI, strengthens specific neural pathways through a process called neuroplasticity. Neuroplasticity refers to the brain’s ability to reorganize and adapt by forming new neural pathways in response to experiences. When a behavior activates the reward center, those neural pathways become stronger, while pathways linked to less immediately gratifying outcomes weaken. Over time, this reinforcement creates a habitual cycle: the more we experience the dopamine “hit” from an instantly gratifying experience, the more likely we are to default to that behavior in the future.
The accessibility and speed of AI tools make it easy to stimulate this reward system. With just a few quick keystrokes, AI users can obtain immediate, tailored answers, often without engaging in the cognitive effort required for deep understanding. This convenience creates a feedback loop in which each time a user receives a quick answer, the brain’s reward circuitry is activated, thus reinforcing the habit of turning to AI rather than grappling with challenging concepts.
Over time, this pattern can foster a dependency on AI for information seeking, mirroring the neural mechanism seen in behavioral addictions. For students, continually turning to AI to avoid the discomfort of effortful thinking can lead to habits that make sustained and independent critical thinking increasingly difficult. While not all habitual AI use rises to the level of addiction, the parallels in neural pathways highlight the need for intentional strategies to promote critical thinking and academic resilience.
To address the impact of dopamine-driven AI dependency on learning, a multi-step, scaffolded approach is recommended. While the accessibility and speed of AI tools may reinforce habits of seeking instant gratification, these same tools can also be used to foster deeper learning and critical thinking when intentionally integrated into the classroom. This process begins with examining initial strategies, progresses through intermediate and advanced techniques, and culminates in implementing a Scholarship of Teaching and Learning (SoTL) framework. The final step involves collecting and analyzing data on faculty teaching and student learning outcomes.
Start with Evidence-Based Learning Strategies
The initial step in this process is educating students about foundational learning principles such as spaced repetition, the forgetting curve, and active learning. These principles promote habits that counterbalance the over-reliance on AI by encouraging cognitive effort and long-term memory consolidation. These concepts highlight that learning is more effective when study sessions are distributed over time rather than massed together, as spaced repetition leverages the natural process of forgetting to improve retention, while active learning engages cognitive processes that strengthen critical thinking. When faculty ask students to create mock exams, build in continual review, and self-assess their own learning, faculty are introducing best practices and helping students move beyond surface-level AI use to promote deeper understanding.
Moreover, faculty can educate students about the benefits and challenges of AI while designing assignments that use these tools to promote critical thinking, rather than simply producing quick answers. For instance, faculty might ask students to compare AI-generated responses with their own analysis, encouraging reflection and iterative learning. This initial stage lays the groundwork for helping students recognize how over-reliance on AI and dopamine-driven instant gratification can influence learning behaviors.
Tips for Faculty:
Design assignments that require students to apply spaced repetition, such as weekly cumulative quizzes or concept maps that build over time. Introduce AI tools in a way that encourages exploration and critical thinking, such as asking students to compare AI-generated responses with their own analysis.
Build Metacognitive Skills
To deepen students’ understanding of AI’s effects in the classroom, faculty can integrate metacognitive strategies into both AI-based and non-AI assignments. For example, requiring students to keep reflective journals throughout a project allows them to document how and when they are using AI, noting moments when AI helped clarify concepts or when it might have caused confusion. This practice helps students recognize their learning habits, identify gaps in understanding, and reduce potential over-reliance on AI.
By focusing on metacognition, students can develop greater ownership over their learning while being equipped to identify early signs of over-reliance, such as reduced critical thinking, weakened problem-solving, and diminished ability to evaluate AI-generated information. If students notice they are not questioning AI-generated answers, struggling with independent problem-solving, or skipping active steps like brainstorming and revising, they can adjust their approach to prioritize critical thinking and active learning strategies.
Tips for Faculty:
Encourage students to maintain reflective journals that track their use of AI tools, focusing on when and how these tools were helpful or problematic. Develop rubrics that reward critical engagement with AI, such as questioning its outputs or integrating its suggestions into broader problem-solving processes.
Continuously Improve Through SoTL
The final strategy is adopting a Scholarship of Teaching and Learning (SoTL) perspective, where faculty collect and analyze data to assess teaching effectiveness and student learning. By treating classrooms as research sites, faculty can continuously refine their approaches to foster deeper engagement and mitigate the risks of AI overuse. For example, analyzing how students interact with AI tools can reveal patterns of dependency or instances where AI enhances learning. Including students as co-investigators in these SoTL studies fosters collaboration, ownership of learning, and reflective practice. This partnership helps faculty and students explore how AI tools can enhance learning without creating dependency.
Tips for Faculty:
Use surveys or focus groups to gather student feedback on how AI tools impact their learning and engagement. Collaborate with students to design research projects that explore the effects of AI and dopamine on learning, fostering a sense of shared inquiry. Analyze patterns of AI use to identify signs of over-reliance.
By progressively integrating these evidence-based learning principles, faculty can effectively address the challenges posed by dopamine-drive behaviors and AI dependency, transforming them into opportunities for deeper learning. The same mechanisms that drive students toward instant gratification can also be harnessed to foster critical thinking and academic resilience when AI is used intentionally. This comprehensive approach empowers both educators and students to adapt, reflect, and thrive with a reduced risk of AI over-reliance in this evolving digital learning environment.
Laura Landon, OTD, OTR/L, is an Assistant Professor of Occupational Therapy at Maryville University in St. Louis, Missouri. Laura earned her doctorate in occupational therapy from Washington University School of Medicine. Currently, within her educator role, she teaches graduate-level OT students within the courses of Anatomy, Biomechanics, Neuroscience, and Cognitive Evaluation and Intervention. Her scholarly interests include innovation in teaching practices, addressing challenges in higher education, and integrating technology in educational spaces.
Michael Kiener, PhD, CRC, is a professor at Maryville University of St. Louis in their Clinical Mental Health Counseling program. For the past 10 years he has coordinated their Scholarship of Teaching and Learning Program, where faculty participate in a yearlong program with a goal of improved student learning. In 2012 and 2024 he received the Outstanding Faculty Award for faculty who best demonstrate excellence in the integration of teaching, scholarship and/or service. He has over thirty publications including a co-authored book on strength-based counseling and journal articles on career decision making, action research, counseling pedagogy, and active and dynamic learning strategies.
References
Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1). https://doi.org/10.3390/soc15010006
Kazmi, S. M., Jilani, A. Q., Ahmad, S., Srivastava, P., Pandey, K., & Anwar, S. (2025). Effects of Excessive Social Media Use on Neurotransmitter Levels and Mental Health: A Neurobiological Meta-Analysis. Era’s Journal of Medical Research, 12(1), 56-60.
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