A Rapid Scoping Review on Academic Integrity and Algorithmic Writing Technologies

This presentation provides insight into the development and findings of a rapid scoping review centred on the intersections of academic integrity and artificial intelligence, with particular attention to algorithmic writing technologies (e.g., ChatGPT) involving faculty, students, teaching assistants, academic student support staff, and educational developers in higher education contexts. This rapid scoping review was developed by a transdisciplinary team including Communication studies, Education, Engineering, and English, and followed Joanna Brigg Institute’s (JBI) updated manual for scoping reviews and the Preferred Reporting Items for Systematic reviews Meta-Analysis (PRISMA) reporting standards. JBI provides a high-quality, trusted framework for conducting these kinds of studies. This inquiry’s study design includes qualitative, quantitative, mixed methods, theoretical and opinion studies; additionally, this inquiry did not restrict studies by geographic location and focused on sources written in English. This review’s studies involved faculty, students, teaching assistants, academic support staff, and educational developers in higher education. It also included studies about artificial intelligence in the context of academic integrity, focusing on artificial intelligence tools that assist text generation and writing developed in Tertiary type A and B postsecondary education. Studies excluded from this review were related to primary and secondary education contexts, did not address the ethical implications of artificial intelligence, and focused on text plagiarism software. The protocol of this rapid review was published in the Canadian Perspectives on Academic Integrity Journal. Its implementation helped this team identify various ethical implications signalled by scholars between 2007 and 2022. Considering the expansive emergence of these technologies and the multiple positionings derived from these new and unprecedented encounters with such technology, we believe that the implications identified in this rapid scoping review are particularly relevant to inform academic staff, administration, students, and academic integrity researchers’ ethical decision-making and practices when teaching, learning, designing, and implementing assessments, and doing research. The findings of this rapid scoping review encompass nuanced perspectives concerning the ethical and unethical uses of these emerging technologies and insights into equity, diversity, and inclusion issues

Artificial Intelligence and Authorship Editor Policy: ChatGPT, Bard Bing AI, and beyond

Artificial intelligence and large-language model chatbots have generated significant attention in higher education, and in research practice. Whether ChatGPT, Bard, Jasper Chat, Socratic, Bing AI, DialoGPT, or something else, these are all shaping how education and research occur. In this Editorial, we offer five editorial principles to guide decision-making for editors, which will also become policy for the Journal of University Teaching and Learning Practice. First, we articulate that non-human authorship does not constitute authorship. Second, artificial intelligence should be leveraged to support authors. Third, artificial intelligence can offer useful feedback and pre-review. Fourth, transparency of artificial intelligence usage is an expectation. And fifth, the use of AI in research design, conduct, and dissemination must comply with established ethical principles. In these five principles, we articulate a position of optimism for the new forms of knowledge and research we might garner. We see AI as a mechanism that may augment our current practices but will not likely replace all of them. However, we do issue caution to the limitations of large language models including possible proliferation of poor-quality research, Stochastic Parroting, and data hallucinations. As with all research, authors should be comfortably familiar with the underlying methods being used to generate data and should ensure a clear understanding of the AI tools being used prior to deployment for research

Assessing the Potential and Risks of AI-Based Tools in Higher Education: Results from an eSurvey and SWOT Analysis

Recent developments related to tools based on artificial intelligence (AI) have raised interests in many areas, including higher education. While machine translation tools have been available and in use for many years in teaching and learning, generative AI models have sparked concerns within the academic community. The objective of this paper is to identify the strengths, weaknesses, opportunities and threats (SWOT) of using AI-based tools (ABTs) in higher education contexts. We employed a mixed methods approach to achieve our objectives; we conducted a survey and used the results to perform a SWOT analysis. For the survey, we asked lecturers and students to answer 27 questions (Likert scale, free text, etc.) on their experiences and viewpoints related to AI-based tools in higher education. A total of 305 people from different countries and with different backgrounds answered the questionnaire. The results show that a moderate to high future impact of ABTs on teaching, learning and exams is expected by the participants. ABT strengths are seen as the personalization of the learning experience or increased efficiency via automation of repetitive tasks. Several use cases are envisioned but are still not yet used in daily practice. Challenges include skills teaching, data protection and bias. We conclude that research is needed to study the unintended consequences of ABT usage in higher education in particular for developing countermeasures and to demonstrate the benefits of ABT usage in higher education. Furthermore, we suggest defining a competence model specifying the required skills that ensure the responsible and efficient use of ABTs by students and lecturers

A New Artificial Intelligence based Internet Online English Teaching Model with Curriculum of Ideological and Political Concern

With the development of artificial intelligence and the rapid spread of the Internet, online teaching has become an increasingly popular method of education. However, in the context of the post-epidemic era of COVID-19, online teaching has become even more important, as many educational institutions have been forced to transition to this model to ensure continuity of learning. In this context, there is a growing need to develop innovative approaches to online teaching that can effectively address the challenges posed by the pandemic. Online teaching has become increasingly important for higher education institutions around the world, and it has been particularly crucial during the COVID-19 pandemic. The teaching of English at universities and colleges exhibited significant performance for online teaching. The ideology concept performs online teaching in English for politics and comprises of different strategies. English teaching, several strategies can be implemented. This research paper proposes a novel approach to integrate artificial intelligence (AI) and cloud computing technologies in the online English teaching model with a curriculum of ideological and political concern for colleges and universities. The proposed model, referred to as AIIOE, aims to enhance the quality and effectiveness of online English teaching while also providing a comprehensive education on ideological and political issues. The AIIOE model utilizes natural language processing (NLP), machine learning, and cloud computing technologies to provide a personalized and interactive learning experience to students. The proposed curriculum includes topics related to political ideology, history, and culture to enhance students' awareness and understanding of their social and political environment. The study adopts a mixed-methods approach, including a survey of English teachers, focus group interviews with students, and an analysis of students' performance in English language proficiency and ideological and political awareness. The results indicate that the AIIOE model significantly improves students' English language proficiency, knowledge of ideological and political issues, and overall learning experience. The examination is evaluated based on the ideological and political curriculum with an Internet-based online teaching mode in English teaching. With the investigation of the Internet online teaching model, the significant contribution is evaluated. Through analysis, it is concluded that the concept of the Internet Online teaching model significantly contributed to ideological and political factors

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education

Enriching Information Technology Course Materials by Using Youtube

IT offers some benefits and collaborations in various sectors. This research focuses on exploring higher education subjects via social technology, YouTube. YouTube is the world largest video based contents application in the world. Current learning materials are not only in text and images, but included video contents. This research enriching students learning materials may involving YouTube as learning sources. The study observed 118 sophomore students in computer science faculty. The results show that, involving YouTube in enriching students course material able to create conductive learning environment. This strategy increases students understanding in their field of study.Comment: Excellent Paper Award of AICSIT2017, 8 page

Chatbots for learning: A review of educational chatbots for the Facebook Messenger

With the exponential growth in the mobile device market over the last decade, chatbots are becoming an increasingly popular option to interact with users, and their popularity and adoption are rapidly spreading. These mobile devices change the way we communicate and allow ever-present learning in various environments. This study examined educational chatbots for Facebook Messenger to support learning. The independent web directory was screened to assess chatbots for this study resulting in the identification of 89 unique chatbots. Each chatbot was classified by language, subject matter and developer's platform. Finally, we evaluated 47 educational chatbots using the Facebook Messenger platform based on the analytic hierarchy process against the quality attributes of teaching, humanity, affect, and accessibility. We found that educational chatbots on the Facebook Messenger platform vary from the basic level of sending personalized messages to recommending learning content. Results show that chatbots which are part of the instant messaging application are still in its early stages to become artificial intelligence teaching assistants. The findings provide tips for teachers to integrate chatbots into classroom practice and advice what types of chatbots they can try out.Web of Science151art. no. 10386

Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

Educational Technology: The influence of theory

In this paper we explore the role of theories in current practice in educational technology. We review a range of writings from the past 30 years on the nature of learning technology research. We discuss influences on learning technologies from the related fields of Artificial Intelligence in Education (AIED) and Human-Computer Interaction (HCI). We identify two groups of theories which have been used. The first group are related to principled decisions about the design of learning materials. The second group influence the ways in which we frame our research on learning. Research in learning technologies in the future will need to draw on both groups of theories. In this paper, we draw on our own experiences as educational technologists and the purpose of the paper is to encourage other educational technologists to join with us in reflecting on their own use of theories

An Intelligent Tutoring System for Teaching the 7 Characteristics for Living Things

Recently, due to the rapid progress of computer technology, researchers develop an effective computer program to enhance the achievement of the student in learning process, which is Intelligent Tutoring System (ITS). Science is important because it influences most aspects of everyday life, including food, energy, medicine, leisure activities and more. So learning science subject at school is very useful, but the students face some problem in learning it. So we designed an ITS system to help them understand this subject easily and smoothly by analyzing it and explaining it in a systematic way. In this paper, we describe the design of an Intelligent Tutoring System for teaching science for grade seven to help students know the 7 characteristics for living things smoothly. The system provides all topics of living things and generates some questions for each topic and the students should answer these questions correctly to move to the next level. In the result of an evaluation of the ITS, students like the system and they said that it is very useful for them and for their studies