Generative AI (GenAI) is increasingly positioned as a transformative tool in education. This work explores how GenAI can enhance reflective practices to foster growth mindsets-an essential trait for resilience, motivation, and lifelong learning. Drawing on frameworks for process-oriented education and ongoing experimentation, we propose an approach leveraging prompt engineering and fine-tuning to create humanlike, iterative feedback on student reflections. To evaluate this, a Turing-test-style experiment is designed to assess whether students can differentiate between AI- and human-generated feedback. By addressing challenges such as feedback scalability, ethical transparency, and student trust, we hope to find ways to design more adaptive, inclusive, and growth-focused educational ecosystems.
Reflective practices are integral to fostering growth mindsets, encouraging students to evaluate their
learning processes and identify areas for improvement. However, many struggle to translate
reflections into actionable outcomes. This gap is where GenAI may prove to be useful. By analyzing
reflection patterns, GenAI tools can offer tailored feedback that highlights themes and provides
actionable suggestions (see also [
Fostering a growth mindset in students hinges on the quality and framing of feedback they receive.
Effective feedback highlights effort, improvement, and actionable steps while avoiding fixed-ability
language [
However, as we have seen, providing such feedback at scale is a persistent challenge in education.
GenAI tools offer a potential solution by generating feedback that mirrors these growth-oriented
principles. For example, AI feedback can be explicitly framed to recognize effort: “Your reflection
shows strong engagement with the topic. Keep building on this effort.”, emphasize process: “Your
challenge in learning new applications of the theory demonstrates a valuable learning opportunity
to refine your approach.”, and provide actionable steps: “Consider setting smaller, achievable goals
to improve your learning in future courses.”
We are preparing an experiment that tests how well AI can replicate these growth-oriented qualities
in feedback. The experiment will assess whether GenAI feedback resonates with students as deeply
as human feedback in promoting growth mindsets. Our methodology integrates prompt engineering
and fine-tuning, allowing GenAI to adapt to specific educational contexts. Prompt engineering
involves strategically crafting inputs to guide AI outputs [
Paradigms like CBL put emphasis on self-reflection which in turn leads to a need for timely processing of student input. This is where applications of GenAI could be introduced. One of the key challenges in integrating GenAI into education is ensuring that the feedback generated by AI is both perceived as valuable and trusted by students. For GenAI tools to support reflective practices effectively, they must emulate the depth, nuance, and context-awareness of human feedback. However, it is equally important to validate whether students can discern differences between AIand human-generated feedback and whether they view AI feedback as credible and actionable. To address this, we propose the experiment will use a Turing-test-style design. This experiment is grounded in two objectives: • •
To evaluate the quality of AI-generated feedback against human standards.
To assess how closely AI feedback aligns with students’ expectations for meaningful and contextually relevant feedback.
The ability of AI tools to analyze patterns in reflective entries, identify gaps, and provide targeted
suggestions makes them invaluable in settings where timely, personalized feedback is traditionally
constrained by resources. However, scaling such tools while maintaining quality introduces a unique
challenge: balancing the need for efficiency with the expectation for human-like responsiveness and
insight. The Turing-test paradigm is particularly well-suited for this challenge. As an example,
modified variants of the Turing test have recently been employed in psychological research to
understand which type of social information people find most likely to be AI-generated [
How do students perceive the quality of AI-generated feedback compared to that of their instructors? Does the feedback from AI meet the pedagogical and emotional needs students associate with human input? To answer these questions, participants will evaluate two feedback samples for each reflection—one generated by a teacher and the other by a GenAI model trained for this specific task with real feedback data. Participants will identify which feedback they believe is human-authored and which is AI-generated. This study provides insights into the perceived authenticity, quality, and relevance of AI feedback. The experiment thus examines whether GenAI feedback can mirror the motivational and process-oriented framing essential for fostering growth mindsets. The data collection for the experiment is planned in February 2025.
The integration of GenAI into reflective practices presents immense opportunities for fostering growth mindsets, but it also requires addressing specific challenges to ensure its ethical, effective, and sustainable use. Insights from the Turing-test experiment provide a foundation for navigating these challenges, enabling thoughtful deployment of GenAI tools in education. This section explores three critical areas that should be addressed to maximize the potential of AI-driven feedback.
For GenAI tools to foster trust among students, it is essential to maintain transparency about their role in generating feedback. Students must understand how AI operates, what its limitations are, and how it complements (rather than replaces) human educators. Lack of transparency can lead to distrust or misconceptions about the reliability of AI-generated feedback, potentially undermining its effectiveness.
For reflective feedback that is written by GenAI to be successfully adopted by students, it is important to clearly communicate to students when and how AI-generated feedback is used, ensuring they view it as a tool to enhance their learning, rather than one that replaces instructors. Students should also be involved in discussions about the role of AI in education, fostering a sense of ownership and engagement. A challenges is to balance simplicity and depth in explanations of the AI’s role in education. Overly technical descriptions might confuse students, while oversimplifications could misrepresent its capabilities.
AI models are susceptible to biases embedded in their training data, which can lead to unfair or unbalanced feedback. For instance, a model trained on predominantly positive and optimistic reflections might struggle to offer constructive criticism to students. Ensuring fair and suitable feedback is crucial for broad adoption.
We are currently collecting data from multiple courses in which students actively reflect on their own growth and development, and as such are building a dataset that represents real student struggles and real instructor feedback. Although the dataset includes reflections from diverse courses, further diversification will be necessary to address biases in language patterns, cultural context, and feedback framing. Also, whether these data will be sufficient to train a model in giving appropriate feedback is yet unknown.
While GenAI excels at automating routine feedback tasks, human oversight remains indispensable for nuanced, complex reflections that require deeper contextual understanding. A hybrid approach, combining AI and human educators, can balance scalability with personalized attention. We are explicitly stating that the goal is not to replace feedback from human instructors, but rather augment it.
Feedback written by GenAI could be checked and edited by instructors to safeguard its quality and appropriateness. This allows educators to focus on higher-order coaching tasks while delegating routine feedback generation to AI. As such, we could use AI feedback as a starting point, enabling educators to refine it for specific cases. Challenges include ensuring that educators have the skills and tools to effectively interpret and build upon AI-generated feedback, and that we should avoid over-reliance on AI, as this would reduce the positive influence of human interactions in learning. In other words, human oversight remains crucial for addressing reflections that delve into personal challenges, ensuring that the feedback considers the emotional and situational context.
The Turing-test experiment offers a unique opportunity to address these challenges by evaluating how students perceive and engage with AI feedback. Insights from the experiment can guide improvements in transparency, reduce biases, and optimize the balance between automation and human input. Through deliberate attention to these challenges, GenAI can move beyond simply scaling feedback to becoming a transformative partner in fostering growth mindsets and reflective learning. This experiment serves as a critical validation step for the integration of GenAI tools into reflective practices. If successful, it will demonstrate not only the capability of GenAI to emulate human feedback but also its potential to enhance the scalability and personalization of reflective education. Importantly, it will shed light on the specific conditions under which students perceive AI-generated feedback as valuable, thus informing the design of future tools and frameworks. By situating students as active evaluators, the Turing-test experiment also fosters transparency and trust in AI systems. It creates a collaborative space where students can critically engage with the evolving role of technology in their learning journey, laying the groundwork for more inclusive and reflective educational ecosystems.
The potential applications of GenAI extend beyond individual courses, offering opportunities to transform broader educational frameworks. GenAI could augment and catalyze the process of extracting insights from student reflections across multiple courses, providing educators with insights into student development trends. This data could inform interventions and curriculum design. Such longitudinal insights could enable educators to tailor curriculum design to address recurring student challenges, fostering a culture of iterative improvement across courses. From formative assessments to personalized learning paths, the scalability of GenAI positions it as a cornerstone of future educational innovations. The integration of Generative AI into reflective practices offers transformative potential for fostering growth mindsets at scale. By providing nuanced, personalized feedback through prompt engineering and fine-tuning, GenAI addresses critical challenges of scalability and quality. The proposed inspired Turing-test experiment provides a rigorous evaluation framework, ensuring that these tools meet the high standards expected in education.
As we continue to explore these opportunities, GenAI emerges as a partner in creating more reflective, resilient, and growth-focused educational ecosystems. Through this integration, we aim to empower students to achieve not only academic success but also the lifelong learning skills necessary to thrive in an ever-evolving world.
Declaration on Generative AI The authors declare that Generative AI tools have not been used to prepare the manuscript.