Model Pembelajaran Generative AI untuk Meningkatkan Kemampuan Berpikir Kritis Mahasiswa

Alan Deta; Beta Fitri Siregar; Ridwan Syahputra

doi:10.69688/jpip.v4i2.213

Authors

Alan Deta Universitas Satya Karya Mahkota
Beta Fitri Siregar Universitas Satya Karya Mahkota
Ridwan Syahputra Universitas Satya Karya Mahkota

DOI:

https://doi.org/10.69688/jpip.v4i2.213

Keywords:

Berpikir Kritis, Generative AI, Kecerdasan Artifisial, Mahasiswa, Model Pembelajaran

Abstract

Pemanfaatan Generative Artificial Intelligence (Generative AI) dalam pendidikan tinggi membuka peluang untuk menghadirkan pembelajaran yang interaktif, adaptif, dan dialogis, tetapi manfaat tersebut bergantung pada desain pedagogis yang menjaga keterlibatan kognitif mahasiswa. Penelitian ini bertujuan mengembangkan dan menguji efektivitas model pembelajaran berbasis Generative AI untuk meningkatkan kemampuan berpikir kritis mahasiswa. Penelitian menggunakan pendekatan kuantitatif dengan desain quasi-experimental pretest-posttest control group. Sebanyak 120 mahasiswa terlibat, terdiri atas kelompok eksperimen (n = 60) dan kelompok kontrol (n = 60). Model pembelajaran dikembangkan dalam lima tahap, yaitu orientasi masalah, eksplorasi dengan Generative AI, verifikasi kritis, kolaborasi dan argumentasi, serta refleksi metakognitif. Data dikumpulkan melalui tes kemampuan berpikir kritis dan lembar validasi model, kemudian dianalisis menggunakan statistik deskriptif, uji-t, N-gain, dan Cohen’s d. Hasil validasi ahli menunjukkan rata-rata kelayakan model sebesar 4,28 dari 5 (kategori sangat baik). Skor berpikir kritis kelompok eksperimen meningkat dari 64,00 menjadi 81,06, sedangkan kelompok kontrol meningkat dari 62,74 menjadi 70,42. Perbedaan posttest antarkelompok signifikan (t = 6,167; p < 0,001) dengan ukuran efek besar (d = 1,126). Temuan menunjukkan bahwa Generative AI lebih berpotensi mendukung berpikir kritis ketika ditempatkan dalam siklus eksplorasi, verifikasi, argumentasi, dan refleksi, bukan sekadar digunakan untuk menghasilkan jawaban.

References

[1] U.N.E.S.C.O., Guidance for Generative AI in Education and Research. Paris, France: UNESCO, 2023.

[2] M. Kasneci, ‘ChatGPT for good? On opportunities and challenges of large language models for education’, Learning and Individual Differences, no. 102274, 2023, doi: 10.1016/j.lindif.2023.102274.

[3] A. Tlili, ‘What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education’, Smart Learning Environments, no. 15, 2023, doi: 10.1186/s40561-023-00237-x.

[4] C. K. Lo, ‘What is the impact of ChatGPT on education? A rapid review of the literature’, Education Sciences, vol. 13, no. 4, Art. no. 410, 2023, doi: 10.3390/educsci13040410.

[5] D. T. K. Ng, J. K. L. Leung, S. K. W. Chu, and M. S. Qiao, ‘Conceptualizing AI literacy: An exploratory review’, Computers and Education: Artificial Intelligence, no. 100041, 2021, doi: 10.1016/j.caeai.2021.100041.

[6] S. M. Laupichler, A. Aster, A. Schirch, and T. Raupach, ‘What do university students know about artificial intelligence? Development and validation of an AI literacy test’, Computers and Education: Artificial Intelligence, no. 100165, 2023, doi: 10.1016/j.caeai.2023.100165.

[7] D. Long and B. Magerko, ‘What is AI literacy? Competencies and design considerations’, in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1-16,. doi: 10.1145/3313831.3376727.

[8] Y. K. Dwivedi, ‘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, no. 102642, 2023, doi: 10.1016/j.ijinfomgt.2023.102642.

[9] G. Grassini, ‘Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings’, Education Sciences, vol. 13, no. 7, Art. no. 692, 2023, doi: 10.3390/educsci13070692.

[10] J. Rudolph, S. Tan, and S. Tan, ‘ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?’, Journal of Applied Learning & Teaching, vol. 6, no. 1, 2023.

[11] D. R. Cotton, P. A. Cotton, and J. R. Shipway, ‘Chatting and cheating: Ensuring academic integrity in the era of ChatGPT’, Innovations in Education and Teaching International, vol. 61, no. 2, pp. 228-239, 2024.

[12] C. K. Y. Chan, ‘A comprehensive AI policy education framework for university teaching and learning’, International Journal of Educational Technology in Higher Education, no. 38, 2023, doi: 10.1186/s41239-023-00408-3.

[13] E. Panadero, ‘A review of self-regulated learning: Six models and four directions for research’, Frontiers in Psychology, no. 422, 2017, doi: 10.3389/fpsyg.2017.00422.

[14] T. Theobald, ‘Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis’, Contemporary Educational Psychology, no. 101976, 2021, doi: 10.1016/j.cedpsych.2021.101976.

[15] U.N.E.S.C.O., AI Competency Framework for Students. Paris, France: UNESCO, 2024.