The study proposes the incorporation of “epistemic hygiene,” understood as the systematic development of competencies in critical judgment, comparison, and validation of knowledge generated by algorithmic systems.
Abstract
This study examines the transformation of learning processes in higher education within contexts mediated by artificial intelligence (AI), focusing on how students evaluate the quality and reliability of knowledge in algorithmic environments. Using a qualitative approach, 20 semi-structured interviews were conducted with undergraduate students in the final two semesters of their program who use AI tools in their academic activities. The analysis identified differentiated patterns in verification practices, levels of cognitive autonomy, and criteria for evaluating information. The findings suggest that students combine active validation strategies with judgments based on the coherence and immediacy of responses. Uneven bias detection was also observed, revealing limitations in the critical evaluation of AI-generated content. The study concludes that the quality of learning in AI-mediated environments depends in part on situated practices of verification, evaluation, and trust regulation. In this regard, the study proposes the incorporation of “epistemic hygiene,” understood as the systematic development of competencies in critical judgment, comparison, and validation of knowledge generated by algorithmic systems.
Artificial intelligence, as one of the most transformative technologies of the present era, has had a profound impact on learning and education processes and is redefining the role of humans, teachers, and educational systems. The aim of this research is to rethink the concept of human learning in the era of artificial intelligence and to explain the opportunities, challenges, and ethical-educational requirements arising from it. The research is fundamental and was conducted with an interpretive qualitative approach combining library research and semi-structured interviews. In the library section, data were extracted from a targeted review of scientific sources between 2019 and 2025, and in the qualitative section, semi-structured interviews were conducted with 16 experts in the fields of education, technology, and AI ethics. Data analysis for the qualitative part was conducted based on Braun and Clarke's (2006) six-step method, and main themes were extracted. The research findings showed that artificial intelligence increases the capacity to improve learning by providing contexts such as personalized learning, intelligent assessment, and real-time feedback. At the same time, it also has limitations and challenges. These limitations and challenges include data bias, privacy violations, weakening of the teacher's role, and cultural incompatibility. As a result, it can be said that learning in the age of artificial intelligence, along with the opportunities and possibilities it creates, also has challenges and limitations. By strengthening opportunities and properly managing challenges, the capacities of artificial intelligence can be used for growth, justice, and excellence in human education.
Shakiba Ghasemi, A. Izadpanah· Assessment and Practice in E...· 0 citations
The concept of the “illusion of teaching competence” is introduced as a distinct theoretical construct, referring to the overestimation of pedagogical expertise derived from technological fluency and instrumental performance, and differentiating it from related constructs such as self-efficacy and competence overestimation.
Mona Bădoi-Hammami, S. Matei, Sorin Ivan et al.· Frontiers in Education· 0 citations
It is concluded that academic institutions must use clear ethical practices and AI-aware assessment designs to ensure that technology is used as an assisting tool that improves users' learning while ensuring the fundamental values of education.
Sugandha Nandedkar, Prachi Waghmare, Ashwini Swami et al.· International Scientific Jou...· 0 citations
The findings show that AI is increasingly seen as a transformative academic tool, especially for research, language learning, writing support and problem‐solving, and despite widespread AI adoption, the study identifies significant gaps in institutional infrastructure and the absence of systematic training programmes.
M. Doğan, B. Kashkhynbay, Zhaniyat Baltabayeva· European Journal of Educatio...· 0 citations
Empirical evidence is contributed from educational action research showing that structured pedagogical interventions can promote the critical, ethical, and responsible use of Generative Artificial Intelligence in education.
Rodrigo Florencio da Silva· Information· 1 citation
There is a gap between the ability to identify correctness and the ability to implement it in effective instructional planning and the need to develop critical thinking in the context of using artificial intelligence in teacher training is emphasized.
S. Cohen, Sarit Kowler, D. Zeitoun· European Journal of Educatio...· 0 citations