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Nurul Haeniah
Indonesian Language Education Study Program, Faculty of Teacher Training and Education, Universitas Sembilanbelas November Kolaka, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Environmental Science Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Artificial Intelligence Literacy and Its Implementation among University Students Nurul Haeniah; Syarifuddin Tundreng; Kadirun Kadirun
Jurnal Inovasi Pendidikan dan Sains Vol 6 No 2 (2025): August
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Nahdlatul Wathan Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51673/jips.v6i2.2486

Abstract

This research aims to overviewof the level of artificial intelligence (AI) literacy and to explore the practices of its implementation among university students. The method used was mixed methods, combining quantitative and qualitative approaches. Quantitative data were collected through a Likert-scale-based questionnaire adapted from the Multidimensional AI Literacy Scale (MAILS) instrument, while qualitative data were obtained through in-depth interviews. The research subjects consisted of 46 individuals, with 7 of them serving as interview respondents. The analysis results showed that the average score of students' artificial intelligence (AI) literacy was 66.83, which fell into the moderate category. A total of 30.43% of university students were in the high category, 58.70% in the moderate category, and 10.87% in the low category. The highest score was found in the eval_uation indicator (71.3%), followed by the ethics of AI use (67.61%), both of which were in the moderate category. The lowest scores were found in the AI usage indicator (65%) and AI detection (65.43%). The interview results revealed that the implementation of artificial intelligence (AI) literacy among university students was still not optimal. This was reflected in their shallow understanding, not optimal usage, low detection ability, eval_uation that was not conducted automatically, and ethical awareness that was situational and inconsistent. In conclusion, university students' artificial intelligence (AI) literacy needs to be comprehensively strengthened to align with the demands of current technological developments.
Effectiveness of Artificial Intelligence and Data Literacy in Case-Based Learning on the Ability to Write Toulmin-Model Argumentative Essays Nurul Haeniah; Andi Saadilah
Jurnal Inovasi Pendidikan dan Sains Vol 6 No 3 (2025): December
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Nahdlatul Wathan Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51673/jips.v6i3.2713

Abstract

This study aims to examine the effectiveness of integrating Artificial Intelligence (AI) and data literacy in Case-Based Learning (CBL) on students’ ability to write argumentative essays using the Toulmin model. The research design used a pre-experimental One-Group Pretest-Posttest Design with 22 student participants. The data were collected through argumentative essay writing tests administered before and after the treatment, and analyzed using the Paired-Samples t-Test. The results of the study showed a significant increase between the pretest score (M=53.59) and the posttest score (M=71.27), with a Sig. (2-tailed) value of 0.000<0.05. All Toulmin indicators increased, especially backing (56.1%) and rebuttal (46.8%), indicating that students’ ability to construct arguments based on evidence and critical analysis became stronger. The study concludes that integrating CBL, AI (ChatGPT), and data literacy effectively strengthens students’ academic argumentative abilities while supporting learning outcomes aligned with Outcome-Based Education (OBE) and the demands of twenty-first-century skills.
Co-Authors Andi Saadilah Kadirun Kadirun Syarifuddin Tundreng