Artificial Intelligence in Higher Education: Challenges and Opportunities’

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the emerging fields in education technology. Whilst it has been around for about thirty years, educators remain unclear as to how to take full pedagogical advantage of AI on a broader scale and how it could actually have a meaningful impact on teaching and learning in higher education. This paper aims to evaluate Artificial Intelligence within Higher Education, focussing on the opportunities and challenges it presents. It also investigates the educational implications of emerging technologies on the way students learn and how institutions teach and evolve. The paper gathers some examples of the introduction of AI in education in a bid to establish equitable, quality education for all. Firstly, the paper analyses how AI can be used to improve learning outcomes, presenting examples of how AI technology can help education systems use data to improve equity and quality in Higher Education. The paper also addresses the benefits and challenges of introducing AI in educational settings, as well as the potential risks of such an endeavour. Finally, we put forward some recommendations for AI in education, with a focus on establishing discussions around the uses, possibilities and risks of AI in education for sustainable development

The Development of Chatbot Provided Registration Information Services for Students in Distance Learning

In recent years, chatbots have become crucial, particularly for assisting students with real-time registration information. This research focused on 1) synthesizing registry works related to information provided for students, 2) designing chatbots and conversation structures in the form of interactive conversations between students and robots for answering questions and providing information tailored to their needs, and 3) examining and evaluating the use of chatbots in providing information services to students, while analyzing the accuracy and suitability of the developed chatbot. This study, based on research and development, utilized a sample consisting of 16 staff directly involved in the provision of registration information to students and 255 undergraduate students from Sukhothai Thammathirat Open University, with respondents being selected through a simple random sampling technique. The synthesis of the research results revealed the following findings: 1) A qualitative study revealed that the registration information related to students, called STOU Journey, consisted of 10 issues, and was required for the whole learning period. 2) The result of the design and development of the chatbot revealed that the developer chatbot could be used on both the website and the LINE application. It was also found that the chatbot could answer most questions correctly and completely. The chatbot responded quickly and was easy to use. The chatbot used language that was easy to understand and natural, while 3) satisfactory evaluation results from 255 undergraduate students showed that overall, students who had used the completed version of the chatbot were satisfied with the use of the chatbot at a high level (Mean = 4.19, SD = 0.98) while they also felt that the chatbot was easy to use (Mean = 4.33, SD = 0.95) and the using the chatbot felt like a natural conversation (Mean = 4.22, SD = 0.99)

The Effectiveness of an Interactive WhatsApp Bot on Listening Skills

The present paper attempted to measure the effectiveness of an interactive WhatsApp bot on the listening skills of Omani English as a Foreign Language (EFL) learners. For this purpose, 40 Omani intermediate EFL learners were divided into two groups: a control and an experimental in a higher education institution. A pretest was conducted to ensure the homogeneity of listening skills among all the participants. While both groups received instructions and exercises on listening in class, an interactive WhatsApp bot was designed for the experimental group to receive more instructions and training without time and place limitations. Later, a posttest and a delayed posttest were conducted to compare learners’ performance. The study results showed smooth progress of both groups in listening exams during the posttest and delayed posttest; however, the experimental group’s performance was significantly high. The findings of the study are efficacious and helpful for teachers and learners

Academic Integrity and Artificial Intelligence in Higher Education Contexts: A Rapid Scoping Review Protocol

This paper presents a protocol with methodological considerations for a rapid scoping review on academic integrity and artificial intelligence in higher education. This protocol follows Joanna Brigg Institute’s (JBI) updated manual for scoping reviews and the Preferred Reporting Items for Systematic reviews Meta-Analysis (PRISMA) reporting standards. This rapid scoping review aims to identify the breadth of the literature reflecting the intersection of academic integrity and artificial intelligence in higher education institutions. The included studies in the review will be analyzed for insight concerning this emerging area, particularly its ethical implications. Our findings will be relevant for academic staff, administration, and leadership in higher education and academic integrity researchers

Framework to Enhance Teaching and Learning in System Analysis and Unified Modelling Language

Cowling, MA ORCiD: 0000-0003-1444-1563; Munoz Carpio, JC ORCiD: 0000-0003-0251-5510Systems Analysis modelling is considered foundational for Information and Communication Technology (ICT) students, with introductory and advanced units included in nearly all ICT and computer science degrees. Yet despite this, novice systems analysts (learners) find modelling and systems thinking quite difficult to learn and master. This makes the process of teaching the fundamentals frustrating and time intensive. This paper will discuss the foundational problems that learners face when learning Systems Analysis modelling. Through a systematic literature review, a framework will be proposed based on the key problems that novice learners experience. In this proposed framework, a sequence of activities has been developed to facilitate understanding of the requirements, solutions and incremental modelling. An example is provided illustrating how the framework could be used to incorporate visualization and gaming elements into a Systems Analysis classroom; therefore, improving motivation and learning. Through this work, a greater understanding of the approach to teaching modelling within the computer science classroom will be provided, as well as a framework to guide future teaching activities