Navigating the Implications of Generative AI in Education, Learning, and Assessment

Technological advancements have helped in the rise of generative artificial intelligence (GenAI), which incorporates tools such as Gemini, ChatGPT, Midjourney, and Claude. The rapid integration of GenAI in the education sector is alarming, as teaching methodologies, learning processes, and assessment strategies have been affected. Such effects result from its ability to generate personalized learning materials while offering immediate feedback. As a result, in collaboration with students, education stakeholders have utilized these capabilities to set rules on their utilization in the classroom. Despite having the capabilities to facilitate personalized learning experiences, provide immediate feedback, and automate administrative tasks, it has several challenges. Key concerns include increased cases of plagiarism, as many students are submitting assignments and projects generated from AI. In addition, the ethical implications of AI usage, such as data privacy, algorithmic bias, and equitable access, have increased. This research adopted a literature review as the methodology. More than fifteen research papers were analyzed to identify various methods that can be used to navigate GenAI implications. Through a systematic review of the past literature, this research identified four strategies to help navigate these implications. These solutions are redesigning the assessment criteria, using an AI assessment scale (AIAS), addressing ethical implications, using micro-credentials, incorporating capstone projects and investing in ongoing research and collaboration. Based on these methods of navigating implications caused by GenAI, the study discusses how these methods will help preserve academic standards. The paper further discusses the need for collaboration among educators, technologists, and policymakers in developing the best practices that harness the advantages of GenAI while mitigating its risks.