The AI Classroom: Revolution, Cheat Engine, or the End of Critical Thinking?

Authors

DOI:

https://doi.org/10.70680/sanskriti.v3i1.37

Keywords:

Academic Integrity, Critical Thinking, Educational Assessment, Generative AI, Pedagogical Innovation

Abstract

The rapid proliferation of generative artificial intelligence (AI) tools like ChatGPT represents a seismic shift for educational paradigms. This article interrogates the multifaceted impact of AI in the classroom, moving beyond the initial panic over its potential as a sophisticated "cheat engine." It argues that while AI poses a genuine threat to traditional assessment methods and could potentially atrophy foundational critical thinking skills if misused, it also presents a revolutionary opportunity. The core challenge lies in re-evaluating the purpose of education in an age of ubiquitous knowledge synthesis. This paper explores a path forward, suggesting that the future lies not in restrictive bans but in strategic integration. By examining models where AI serves as a co-pilot for creativity and complex problem-solving, the article proposes a framework for educators to design assessments that leverage AI's capabilities while deepening, rather than replacing, human cognition. The conclusion posits that the ultimate outcome revolution, regression, or renaissance depends on our willingness to adapt pedagogical foundations for a new, collaborative intelligence.

Author Biography

  • Chandan Kumar Barmmon, The Daily Ittefq

     

References

Biesta, G. (2015). What is education for? On good education, teacher judgement, and educational professionalism. European Journal of Education, 50(1), 75–87. https://doi.org/10.1111/ejed.12109

Biggs, J., & Tang, C. (2011). Teaching for quality learning at university (4th ed.). McGraw-Hill Education.

Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21(1), 5–31. https://doi.org/10.1007/s11092-008-9068-5

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027

Bozkurt, A. (2023). Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shift. Asian Journal of Distance Education, 18(1), 1-8. https://www.asianjde.com/ojs/index.php/AsianJDE/article/view/718

Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., ... & Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 1-29. https://www.asianjde.com/ojs/index.php/AsianJDE/article/view/709

Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology, Vol. 2. Research designs: Quantitative, qualitative, neuropsychological, and biological (pp. 57–71). American Psychological Association. https://doi.org/10.1037/13620-004

Brinkmann, S., & Kvale, S. (2015). InterViews: Learning the craft of qualitative research interviewing (3rd ed.). Sage Publications.

Carr, N. (2020). The glass cage: Automation and us. W. W. Norton & Company.

Costa, A. L., & Garmston, R. J. (2016). Cognitive coaching: Developing self-directed leaders and learners (3rd ed.). Rowman & Littlefield.

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage Publications.

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Elsen-Rooney, M. (2023, January 3). NYC education department blocks ChatGPT on school devices, networks. Chalkbeat New York. https://ny.chalkbeat.org/2023/1/3/23537987/nyc-schools-ban-chatgpt-writing-artificial-intelligence

Facione, P. A. (2015). Critical thinking: What it is and why it counts. Insight Assessment. https://www.insightassessment.com/wp-content/uploads/ia/pdf/whatwhy.pdf

Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80–92. https://doi.org/10.1177/160940690600500107

Firestone, W. A. (1993). Alternative arguments for generalizing from data as applied to qualitative research. Educational Researcher, 22(4), 16–23. https://doi.org/10.3102/0013189X022004016

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906

Hutchins, E. (1995). Cognition in the wild. The MIT Press.

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., ... & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2(2), e0000198. https://doi.org/10.1371/journal.pdig.0000198

Lee, H., Huang, Y., & Hwang, G. (2023). A review of the research on the impact of generative AI on higher education. Computers and Education: Artificial Intelligence, 5, 100165. https://doi.org/10.1016/j.caeai.2023.100165

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779

Miles, M. B., Huberman, A. M., & Saldaña, J. (2020). Qualitative data analysis: A methods sourcebook (4th ed.). Sage Publications.

Mollick, E. (2022, December 13). Centaurs and cyborgs on the jagged frontier. One Useful Thing. https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the-jagged

Mollick, E. R., & Mollick, L. (2022). New modes of learning enabled by AI chatbots: Three methods and assignments. SSRN. http://dx.doi.org/10.2139/ssrn.4300783

Newmann, F. M., Carmichael, D. L., & King, M. B. (2016). Authentic intellectual work: Improving teaching for rigorous learning. Corwin.

Pangrazio, L., & Sefton-Green, J. (2021). Digital rights, digital citizenship and digital literacy: What's the difference? Journal of New Approaches in Educational Research, 10(1), 15-27. https://doi.org/10.7821/naer.2021.1.616

Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). Sage Publications.

Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2). https://doi.org/10.53761/1.20.02.07

Perrigo, B. (2023, January 18). Exclusive: The $2 per hour workers who made ChatGPT safer. Time. https://time.com/6247678/openai-chatgpt-kenya-workers/

Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023). Can AI-generated text be reliably detected? arXiv preprint arXiv:2303.11156. https://doi.org/10.48550/arXiv.2303.11156

Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776-778. https://doi.org/10.1126/science.1207745

Stake, R. E. (1995). The art of case study research. Sage Publications.

Su, J., Yang, W., & Zhong, Y. (2023). Exploring the potential of AI-generated text for educational equity: A systematic review. Computers and Education: Artificial Intelligence, 4, 100135. https://doi.org/10.1016/j.caeai.2023.100135

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. https://doi.org/10.1007/978-1-4419-8126-4

Warschauer, M. (2023). The paradox of promise: The impact of AI on education in the digital age. In The Routledge Handbook of Language and the Digital Society (pp. 245-260). Routledge.

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., ... & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), 1-38. https://doi.org/10.1007/s40979-023-00146-z

Wiggins, G. (1990). The case for authentic assessment. Practical Assessment, Research, and Evaluation, 2(1), 2. https://doi.org/10.7275/ffb1-mm19

Zhang, R., Tiwari, P., & Wang, X. (2023). Assistive AI: The potential of large language models for students with learning disabilities. Journal of Learning Analytics, 10(2), 45-60. https://doi.org/10.18608/jla.2023.7800

Zhou, J., Zhang, Y., & Luo, L. (2023). Assisted thinking: A new paradigm for human-AI collaboration in education. Educational Technology & Society, 26(1), 112-125. https://www.jstor.org/stable/48707972

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.

Downloads

Published

24.04.2026

Issue

Section

Articles

How to Cite

The AI Classroom: Revolution, Cheat Engine, or the End of Critical Thinking?. (2026). Sanskriti: Journal of Humanities, 3(1), 1-15. https://doi.org/10.70680/sanskriti.v3i1.37