Artificial Intelligence and Academic Integrity

Integrating Artificial Intelligence (AI) in education presents opportunities and challenges. AI technologies offer innovative solutions to enhance learning experiences (Hassabis et al., 2017). However, the proliferation of AI-powered tools poses significant threats to academic integrity, particularly in facilitating cheating and plagiarism (He et al., 2019). These tools, such as essay generators and paraphrasing tools, undermine the value of authentic learning and assessment, eroding academic integrity (He et al., 2019). Moreover, the sophistication of AI-driven cheating tools makes it harder for educators to detect and prevent academic dishonesty, leading to a burden on institutions to stay ahead of evolving cheating tactics.

In addition, the rise of AI poses challenges to traditional assessment methods, with automated grading systems lacking the nuanced understanding and subjective judgment that human graders bring to the table, potentially leading to biases in assessment outcomes (Chan & Zary, 2019). Furthermore, AI enables the creation of deepfake academic credentials, posing a risk to the credibility of educational qualifications and undermining trust in academic institutions (Barua et al., 2020).

To address these threats, educational institutions must adopt proactive measures to safeguard academic integrity. This includes implementing robust policies for detecting and addressing cheating and plagiarism and critically evaluating the use of AI-driven assessment tools to ensure they complement human judgment and insight (He et al., 2019). Collaboration between educators, technologists, and policymakers is essential to develop ethical guidelines and standards for the responsible use of AI in education, ensuring transparency, accountability, and data privacy rights.

Ultimately, while AI holds promise to transform education for the better, it also presents significant risks to academic integrity that must be addressed through a culture of integrity, accountability, and ethical use of technology (Hassabis et al., 2017; He et al., 2019; Chan & Zary, 2019; Barua et al., 2020).

References:

Barua, I., Vinsard, D., Jodal, H., Løberg, M., Kalager, M., Holme, Ø., … & Mori, Y. (2020). Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis. Endoscopy, 53(03), 277-284. https://doi.org/10.1055/a-1201-7165

Chan, K. and Zary, N. (2019). Applications and challenges of implementing artificial intelligence in medical education: integrative review. Jmir Medical Education, 5(1), e13930. https://doi.org/10.2196/13930

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258. https://doi.org/10.1016/j.neuron.2017.06.011

He, J., Baxter, S., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30-36. https://doi.org/10.1038/s41591-018-0307-0