Artificial intelligence has been attracting the attention of educational researchers recently, especially ChatGPT as a generative artificial intelligence tool. The context of generative artificial intelligence could impact different aspects of students’ learning, such as the motivational aspect. The present research intended to investigate the characteristics of students’ task motivation in the artificial intelligence context, specifically in the ChatGPT context. The researchers interviewed 15 students about their experiences with ChatGPT to collect data. The researchers used inductive and deductive content analysis to investigate students’ motivation when learning with ChatGPT. To arrive at the categories and sub-categories of students’ motivation, the researchers used the MAXQDA 2022. Five main categories emerged: task enjoyment, reported effort, result assessment, perceived relevance, and interaction. Each category comprised at least two sub-categories, and each sub-category was further organized into codes. The results indicated more positive characteristics of motivation than negative ones. The previous results could be due to the conversational or social aspect of the chatbot, enabling relationships with humans and enabling the maintenance of good quality conversations with them. We conclude that a generative AI could be utilized in educational settings to promote students’ motivation to learn and thus raise their learning achievement.

In recent years, artificial intelligence (AI) has emerged as a valuable resource for teaching and learning, and it has also shown promise as a tool to help solve problems. A tool that has gained attention in education is ChatGPT, which supports teaching and learning through AI. This research investigates the difficulties faced by ChatGPT in comprehending and responding to chemistry problems pertaining to the topic of Introduction to Material Science. By employing the theoretical framework proposed by Holme et al., encompassing categories such as transfer, depth, predict/explain, problem solving, and translate, we evaluate ChatGPT’s conceptual understanding difficulties. We presented ChatGPT with a set of thirty chemistry problems within the Introduction to Material Science domain and tasked it with generating solutions. Our findings indicated that ChatGPT encountered significant conceptual knowledge difficulties across various categories, with a notable emphasis on representations and depth, where difficulties in representations hindered effective knowledge transfer.

The implementation of mobile learning seems to be an emerging topic in many educational institutions. As recently noticed, mobile technology has employed wireless technologies to communicate, think, learn, and share in order to spread and exchange information. Therefore, using mobile technologies in learning and teaching can create a positive environment in higher education. Hence, the purpose of this study is to evaluate mobile learning engagement among educational technology students. Data from three focus group discussions and 15 semi-structured interviews with students who experienced mobile learning were gathered using a qualitative approach design. A total of seventeen basic themes and four organizing themes were extracted, where the researchers categories of engagement, i.e., social engagement, cognitive engagement, emotional engagement, and behavioral engagement. In the present research, the findings indicate that social engagement themes included social–mobile interaction, building community, developing relationships, and competition. The cognitive engagement themes included attention, cognitive and meta-cognitive strategies, immersion, and cognitive curiosity. Emotional engagement themes included excitement and enjoyment, instructor comforting students, motivation, and emotional safety. Behavioral engagement themes included effort and time on task, attendance, participation, and positive conduct.

Project-based learning is suggested for independent and collaborative learning that could positively impact students’ learning. This study aimed to identify the role of project-based language learning in developing life skills of students through studying a case of a language class that included 80 students in two grade 6 classes in a private school. The study attempted to answer the following question: What is the role of project-based learning in developing students’ life skills in an Arabic language class? To answer this question, we adopted a theoretical framework that included the following categories of life skills: personal and collaborative skills, self-orientation skills, and collective responsibility. The following three data collecting tools were used during the study: observation, semi-structured interviews, and the teacher and students’ documents. Data collection lasted for two months from late September 2019 to December 2019. Data analysis followed the thematic analyses framework, by categorizing data into themes. The research results indicated that project-based learning helps to improve language students’ personal and cooperative skills through developing their communication skills between themselves or with their teacher. In addition, project-based learning developed language students’ mutual respect, their confidence, and their self-regulation of learning the Arabic language.

Saturation is a key construct used by qualitative research theorists as a way to verify the rigor and validity of a study. This study intends to examine how saturation is used within qualitative educational technology studies that addressed saturation. We selected journals which (a) follow a peer review process; (b) are considered influential in their fields and of high quality, as reflected in being in Q1 (first quartile) or Q2 (second quartile) in Scopus; and (c) publish qualitative research. The number of journals selected for this study was 20, and we selected 3 articles from each. The methodology in the chosen articles was interview-based, and the articles were published in the journal in the 5 recent years 2018–2022 (i.e., a 5-year review period). The research results indicated that 20 out of 60 of the participating studies paid attention to the definition of saturation, 25 out of 60 participating studies paid attention to ‘why saturation was sought’, and 26 out of 60 participating studies paid attention to ‘when saturation was sought’. All the participating studies paid attention to ‘how was saturation assessed’. The most-mentioned expression to indicate the achievement of saturation was the emergence of new information, themes, categories, and codes, where most of the participating studies used expressions related to this aspect.

Students’ perceptions of tools with which they learn affect the outcomes of this learning. GenAI tools are new tools that have promise for students’ learning, especially higher education students. Examining students’ perceptions of GenAI tools as learning tools can help instructors better plan activities that utilize these tools in the higher education context. The present research considers four components of students’ perceptions of GenAI tools: efficiency, interaction, affect, and intention. To triangulate data, it combines the quantitative and the qualitative methodologies, by using a questionnaire and by conducting interviews. A total of 153 higher education students responded to the questionnaire, while 10 higher education students participated in the interview. The research results indicated that the means of affect, interaction, and efficiency were significantly medium, while the mean of intention was significantly high. The research findings showed that in efficiency, affect, and intention, male students had significantly higher perceptions of AI tools than female students, but in the interaction component, the two genders did not differ significantly. Moreover, the degree affected only the perception of interaction of higher education students, where the mean value of interaction was significantly different between B.A. and Ph.D. students in favor of Ph.D. students. Moreover, medium-technology-knowledge and high-technology-knowledge students differed significantly in their perceptions of working with AI tools in the interaction component only, where this difference was in favor of the high-technology-knowledge students. Furthermore, AI knowledge significantly affected efficiency, interaction, and affect of higher education students, where they were higher in favor of high-AI-knowledge students over low-AI-knowledge students, as well as in favor of medium-AI-knowledge students over low-AI-knowledge students.