Systematic Review

Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown the capability of generating human-like conversation on extensive topics. Due to the impressive performance on a variety of language-related tasks (e.g., open-domain question answering, translation, and document summarization), one can envision the far-reaching impacts that can be brought by the LLMs with broader real-world applications (e.g., customer service, education and accessibility, and scientific discovery).

The integrative literature review addresses the conceptualization and implementation of AI Literacy (AIL) in Higher Education (HE) by examining recent research literature.

The rise of generative large language models (LLMs) has opened new opportunities for automating knowledge representation through concept maps, a long-standing pedagogical tool valued for fostering meaningful learning and higher-order thinking. Traditional construction of concept maps is labor-intensive, requiring significant expertise and time, limiting their scalability in education.

Since its launch in late 2022, ChatGPT has ignited widespread interest in Large Language Models (LLMs) and broader Artificial Intelligence (AI) solutions. As this new wave of AI permeates various sectors of society, we are continually uncovering both the potential and the limitations of existing AI tools. The need for adjustment is particularly significant in Computer Science Education (CSEd), as LLMs have evolved into core coding tools themselves, blurring the line between programming aids and intelligent systems, and reinforcing CSEd's role as a nexus of technology and pedagogy.

The concept of Machine Unlearning (MU) has gained popularity in various domains due to its ability to address several issues in Machine Learning (ML) models, particularly those related to privacy, security, bias mitigation, and adaptability. With these abilities, MU is evolving into a promising technology in upholding Responsible AI principles and optimizing ML models' performance. However, despite its promising potential, the concept has not received much attention in the education sector.

Powerful artificial intelligence (AI) tools that have emerged in recent years -- including large language models, automated coding assistants, and advanced image and speech generation technologies -- are the result of monumental human achievements. These breakthroughs reflect mastery across multiple technical disciplines and the resolution of significant technological challenges. However, some of the most profound challenges may still lie ahead.

This systematic review explores the burgeoning intersection of Artificial Intelligence (AI) applications and self-regulated learning (SRL) in higher education. Aiming to synthesize empirical studies, we employed a qualitative approach to scrutinize AI ïs role in supporting SRL processes. Through a meticulous selection process adhering to PRISMA guidelines, we identified 14 distinct studies that leveraged AI applications, including chatbots, adaptive feedback systems, serious games, and e-textbooks, to support student autonomy.

Since the 1990s, there have been heated debates about how evidence should be used to guide teaching practice and education policy, and how educational research can generate robust and trustworthy evidence. This paper reviews existing debates on evidence-based education and research on the impacts of AI in education and suggests a new conceptualisation of evidence aligned with an emerging learning-oriented model of science-for-policy, which we call S4P 3.0.

Improving artificial intelligence (AI) literacy has become an important consideration for academia and industry with the widespread adoption of AI technologies. Collaborative learning (CL) approaches have proven effective for information literacy, and in this study, we investigate the effectiveness of CL in improving AI knowledge and skills. We systematically collected data to create a corpus of nine studies from 2015-2023. We used the Interactive-Constructive-Active-Passive (ICAP) framework to theoretically analyze the CL outcomes for AI literacy reported in each.

Due to perceptions of efficiency and significant productivity gains, various organisations, including in education, are adopting Large Language Models (LLMs) into their workflows. Educator-facing, learner-facing, and institution-facing LLMs, collectively, Educational Large Language Models (eLLMs), complement and enhance the effectiveness of teaching, learning, and academic operations. However, their integration into an educational setting raises significant cybersecurity concerns.