Online learning has become increasingly popular, making the learning process more attractive. One of the most popular learning media is artificial intelligence (AI). However, students do not accept this technology at all. Therefore, this study examined the factors influencing accounting students' acceptance of AI in learning. The survey was conducted with 147 higher-education students who use AI as a learning medium. The data were analyzed using SmartPLS 4.0 with the partial least square approach. The results showed that perceived usefulness influenced behavioral intention to use and satisfaction. However, perceived ease of use was only significant for satisfaction. Similarly, perceived confidence must be consistent with intention. Although it may influence perceived usefulness, other constructs, such as AI quality and personal innovativeness, can increase students' perceptions of the benefits and convenience of adopting AI in learning. Thus, this study contributes to the development of the technology acceptance model (TAM) and the information systems success model and is helpful to scholars, especially in applying AI in learning. They need to pay attention to the quality of AI, such as the accuracy of the information produced. Thus, the need to control the information from the AI only serves as a reference without requiring you to trust it completely.

Higher education institutions have focused their efforts on promoting research seedbeds as a strategy for formative research. In this regard, the impact of such a strategy remains unknown due to the lack of models that enable its evaluation. Therefore, this study aimed to design an evaluation model for the academic productivity of research seedbeds based on the available evidence in the literature. To achieve this, a systematic review was conducted following the PRISMA model, analyzing 53 documents including articles, book chapters, and conference proceedings from the SCOPUS, ProQuest, Jstor, Scielo, and ScienceDirect databases. The results identified indicators that allowed for the design of a model based on six constructs: research training, institutional capabilities, bibliographic production, innovation and development, social appropriation of knowledge, and human resource training. It was concluded that the indicators evaluating research seedbeds seek greater scientific development involving students and improving the quality of research products, which directly impacts the institutional research mission.

This study aimed to evaluate the impact of problem-based learning (PBL) within a teaching unit on the advancement of ethical reasoning and decision-making skills among Israeli female tertiary students. Employing a quasi-experimental design, 48 female students were distributed into three groups, with two serving as control groups receiving conventional curriculum-based instruction and one as an experimental group exposed to the PBL methodology. Both before and after implementation, all groups underwent assessments using a decision-making competency test and an ethical reasoning scale. The results unequivocally demonstrated the significant enhancement of decision-making abilities and ethical thinking through the implementation of the PBL strategy. Comparative analysis revealed substantial improvements in the experimental group compared to the control groups, emphasizing the efficacy of PBL in fostering comprehensive skill development. Furthermore, a positive correlation between ethical thinking and decision-making skills further reinforces the beneficial outcomes associated with PBL. These findings advocate for the widespread integration of PBL techniques across various academic disciplines.

In this article, we present the results of empirical research using a combination of quantitative and qualitative methodology, in which we examined the achievements and difficulties of sixth-grade Slovenian primary school students in decimal numbers at the conceptual and procedural knowledge level. The achievements of the students (N = 100) showed that they statistically significantly (z = -7,53, p < .001) better mastered procedural knowledge (M = 0.60, SD = 0.22) than conceptual knowledge (M = 0.37, SD = 0.17) of decimal numbers. Difficulties are related to both procedural and conceptual knowledge, but significantly more students have difficulties at the level of conceptual knowledge. At the level of procedural knowledge, or in the execution of arithmetic operations with decimal numbers, we observed difficulties in transforming text notation into numerical expressions, difficulties in placing the decimal point in multiplication and division, and insufficient automation of mathematical operations with decimal numbers. At the level of conceptual knowledge of decimal numbers, the results indicate difficulties for students in understanding the place values of decimal numbers, in estimating the sum, product and quotient of decimals with reflection and in mathematical justification. In relation to difficulties in justification, we observed an insufficient understanding of the size relationship between decimal numbers and difficulties in expressing them in mathematical language. The results indicate that to overcome such difficulties in the learning and teaching of mathematics, more balance between procedural and conceptual knowledge is needed.

This article investigates artificial intelligence (AI) implementation in higher education (HE) from experts' perspectives. It emphasises the view of AI's involvement in administrative activities in higher education, experts' opinions concerning the influence of the incorporation of AI on learning and teaching, and experts' views on applying AI specifically to assessment, academic integrity, and ethical considerations. The study used a qualitative method based on an unstructured qualitative interview with open-ended questions. The participants were thirteen individuals currently involved with higher education institutions and had various talents related to AI and education. Findings stress that implementing AI technology in administrative roles within higher education institutions is essential since it cuts costs, addresses problems efficiently and effectively, and saves time. The findings also revealed that AI plays a vital role in learning and teaching by speeding up the learning process, engaging learners and tutors, and personalising learning depending on the learner's needs within an entirely intelligent environment. AI can produce an accurate, objective, and suitable level of assessment. AI aids students in developing a stronger sense of integrity in their academic work by guiding them through AI-powered applications. AI must adhere to ethical laws and policies, ensuring its potential negative aspects are not overlooked or left unchecked.

Analytical thinking is crucial for developing problem-solving, decision-making, and higher-order thinking skills. Many researchers have consistently developed learning management models to enhance students' analytical thinking, resulting in extensive knowledge but lacking clear systematic summaries. This study aims to: (a) explore the effect sizes and research characteristics influencing students' analytical thinking, and (b) compare the effect sizes of learning management models after adjusting for propensity score matching. In exploring 131 graduate research papers published between 2002 and 2021, the research utilized forms for recording research characteristics and questionnaires for assessing research quality for data collection. Effect sizes were calculated using Glass's method, while data analysis employed random effects, fixed effects, and regression meta-analysis methods. The findings indicate that (a) research on learning management models significantly impacts students' analytical thinking at a high level (d̅ = 1.428). Seven research characteristics, including year of publication, field of research, level, duration per plan, learning management process, measurement and evaluation, and research quality, statistically influence students' analytical thinking, and (b) after propensity score matching, learning through techniques such as KWL, KWL-plus, Six Thinking Hats, 4MAT, and Mind Mapping had the highest influence on students' analytical thinking. Recommendations for developing students' analytical thinking involve creating a learning management process that fosters understanding, systematic practical training, expanding thinking through collaborative exchanges, and assessments using learning materials and tests to stimulate increased analytical thinking.