Bilad, M. R., Yaqin, L. N., & Zubaidah, S. (2023). Recent Progress in the Use of Artificial Intelligence Tools in Education. Full text
Abstract: The use of artificial intelligent (AI) tools in education has had a significant impact on learning experiences and outcomes. This review looks at recent advances in artificial intelligence tools and their implications for future research and practice... Overall, this review summarizes recent advances in the use of artificial intelligence tools to revolutionize education, emphasizing the importance of ongoing research, cross-disciplinary collaboration, and careful implementation.
García-Martínez, I., et al. (2023). Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-Analysis. Full text
Abstract: Artificial intelligence (AI) and computational sciences have aroused a growing interest in education. Despite its relatively recent history, AI is increasingly being introduced into the classroom through different modalities, with the aim of improving student achievement. Thus, the purpose of the research is to analyse, quantitatively and qualitatively, the impact of AI components and computational sciences on student performance. For this purpose, a systematic review and meta-analysis have been carried out in WOS and Scopus databases. After applying the inclusion and exclusion criteria, the sample was set at 25 articles. The results support the positive impact that AI and computational sciences have on student performance, finding a rise in their attitude towards learning and their motivation, especially in the STEM (Science, Technology, Engineering, and Mathematics) areas. Despite the multiple benefits provided, the implementation of these technologies in instructional processes involves a great educational and ethical challenge for teachers in relation to their design and implementation, which requires further analysis from the educational research. These findings are consistent at all educational stages.
Kumar, V. V. & Raman, R, (2022). Student Perceptions on Artificial Intelligence (AI) in higher education. Full text Full text PDF
Abstract: This purpose of this study was to understand the student perceptions on the usage of Artificial Intelligence (AI) in higher education. AI could form part of higher education in multiple ways whether it be in the Teaching Learning process, Admission process, the Placement process or the Administrative process. The paper brings out the student perceptions on AI usage in business schools from those enrolled in full time program in business management. Online questioner was used to collect both quantitative and qualitative response. Data was collected from 682 student and statistical analysis have been used for arriving at conclusions. Ordination regression and correlation have been used for analysis of the data collect. The qualitative response was helpful to get the views that students have. The results indicate that student have a perception that AI can be effectively used in teaching learning process, academic administration processes, and should not be used in a few processes related to admission, examination and placements.
Rodway, P. and A. Schepman (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Full text
Abstract: Artificial Intelligence (AI) applications for education are being developed at an increasing pace. It seems reasonable to assume that these applications would enhance student experiences and course satisfaction, and that therefore educational institutions should invest in these technologies to enhance their student offer. However, this should be tested empirically. In the current study a gender-balanced sample of 302 UK students rated course satisfaction, completed the General Attitudes towards AI Scale (GAAIS), comfortableness with AI educational applications, and course satisfaction if AI educational applications were adopted. Although students were, on average, moderately comfortable with AI educational applications, course satisfaction dropped in response to their hypothetical adoption. AI applications that assigned summative grades or that offered wellbeing support gave rise to the highest levels of discomfort. Students were more comfortable with career support, formative course support, and administrative support. Positive and Negative AI attitudes predicted the satisfaction difference, with mediation via comfortableness with applications. We recommend that Higher Education Institutions exercise caution before making major investments in AI educational applications.
Yilmaz, R. & F. G. Karaoglan Yilmaz (2023). The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Full text
Abstract: ChatGPT (generative pre-trained transformer) is one of the artificial intelligence (AI) technologies that have started to be used in programming education. However, the effect of using ChatGPT in programming education on learning processes and outcomes is not yet known. This study investigated the effect of programming education using the ChatGPT on students' computational thinking skills, programming self-efficacy, and motivation toward the lesson. The research was conducted on 45 undergraduate students who took a university-level programming course. The research was carried out according to the experimental design with the pretest-posttest control group. Students were randomly divided into experimental (n = 21) and control (n = 24) groups. While the experimental group students benefited from the ChatGPT during the weekly programming practices, the control group students did not use this tool. Research data were obtained through the computational thinking scale, computer programming self-efficacy scale, and learning motivation in computer programming courses scale. Research findings revealed that the experimental group students' computational thinking skills, programming self-efficacy, and motivation for the lesson were significantly higher than the control group students. In line with this result, it can be said that it may be useful to benefit from AI technologies such as ChatGPT in programming trainings. The research findings, it was emphasized how the most effective use of AI support in the lessons could be made, and various suggestions were made for researchers and educators in this regard.
World Health Organization. (2023). Regulatory considerations on artificial intelligence for health. World Health Organization. Full text PDF
Summary: This publication, which is based on the work of the WG-RC, aims to deliver an overview of regulatory considerations on AI for health that covers the following six general topic areas: documentation and transparency, the total product lifecycle approach and risk management, intended use and analytical and clinical validation, data quality, privacy and data protection, and engagement and collaboration. This overview is not intended as guidance or as a regulatory framework or policy. Rather, it is a discussion of key regulatory considerations and a resource that can be considered by all relevant stakeholders – including developers who are exploring and developing AI systems, regulators and policy-makers who in the process of identifying approaches to manage and facilitate AI systems, manufacturers who design and develop AI-enabled medical devices, and health practitioners who deploy and use such medical devices and AI systems. Consequently, the WG-RC recommends that stakeholders take into account the following considerations as they continue to develop frameworks and best practices for the use of AI in health care and therapeutic development.
Dell'Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., Lakhani, K. R. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 24-013, Available at SSRN: https://ssrn.com/abstract=4573321 or http://dx.doi.org/10.2139/ssrn.4573321
Abstract: The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine the performance implications of AI on realistic, complex, and knowledge-intensive tasks. The pre-registered experiment involved 758 consultants comprising about 7% of the individual contributor-level consultants at the company. After establishing a performance baseline on a similar task, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access, or GPT-4 AI access with a prompt engineering overview. We suggest that the capabilities of AI create a “jagged technological frontier” where some tasks are easily done by AI, while others, though seemingly similar in difficulty level, are outside the current capability of AI. For each one of a set of 18 realistic consulting tasks within the frontier of AI capabilities, consultants using AI were significantly more productive (they completed 12.2% more tasks on average, and completed task 25.1% more quickly), and produced significantly higher quality results (more than 40% higher quality compared to a control group). Consultants across the skills distribution benefited significantly from having AI augmentation, with those below the average performance threshold increasing by 43% and those above increasing by 17% compared to their own scores. For a task selected to be outside the frontier, however, consultants using AI were 19 percentage points less likely to produce correct solutions compared to those without AI. Further, our analysis shows the emergence of two distinctive patterns of successful AI use by humans along a spectrum of human-AI integration. One set of consultants acted as “Centaurs,” like the mythical halfhorse/half-human creature, dividing and delegating their solution-creation activities to the AI or to themselves. Another set of consultants acted more like “Cyborgs,” completely integrating their task flow with the AI and continually interacting with the technology.
