The recent emergence of generative AI (GenAI) tools such as ChatGPT, Midjourney, and Gemini have introduced revolutionary capabilities that are predicted to transform numerous facets of society fundamentally. In higher education (HE), the advent of GenAI presents a pivotal moment that may profoundly alter learning and teaching practices in aspects such as inaccuracy, bias, overreliance on technology and algorithms, and limited access to educational AI resources that require in-depth investigation. To evaluate the implications of adopting GenAI in HE, a team of academics and field experts have co-authored this paper, which analyzes the potential for the responsible integration of GenAI into HE and provides recommendations about this integration. This paper recommends strategies for integrating GenAI into HE to create the following positive outcomes: raise awareness about disruptive change, train faculty, change teaching and assessment practices, partner with students, impart AI learning literacies, bridge the digital divide, and conduct applied research. Finally, we propose four preliminary scale levels of a GenAI adoption for faculty. At each level, we suggest courses of action to facilitate progress to the next stage in the adoption of GenAI. This study offers a valuable set of recommendations to decision-makers and faculty, enabling them to prepare for the responsible and judicious integration of GenAI into HE.

This study aims to examine how 21st-century skills are implemented in the Israeli education system. For a better, in-depth understanding of the reasons underlying the manner in which said skills are implemented among educators in Israel, Hofstede's cultural dimension theory was applied as an analytical tool. This is a pioneer research and contributes to the scientific literature in the realms of education and multiculturalism. Study findings indicate the importance of understanding the relationships between cultural dimensions and cooperative education, creative teaching, motivation for teaching, teaching empathy, and implementing the multicultural approach in teaching and learning processes. These findings have implications on the development of skills, knowledge, and values required to shape the education system's graduates. These young people will integrate into the global labor market in the future and steer Israeli society towards social change centered on parity and egalitarianism.

The integration of generative artificial intelligence (GenAI) in higher education has opened new avenues for enhancing academic writing through student–chatbot interactions. While initial research has explored this potential, deeper insights into the nature of these interactions are needed. This study characterizes graduate students’ interactions with AI chatbots for academic writing, focusing on the types of assistance they sought and their communication style and tone patterns. To achieve this, individual online sessions were conducted with 43 graduate students, and their chatbot interactions were analyzed using qualitative and quantitative methods. The analysis identified seven distinct types of assistance sought by students. The most frequent requests involved content generation and expansion, followed by source integration and verification, and then concept clarification and definitions. Students also sought chatbot support for writing consultation, text refinement and formatting, and, less frequently, rephrasing and modifying content and translation assistance. The most frequent communication style was “requesting,” marked by direct appeals for assistance, followed by “questioning” and “declarative” styles. In terms of communication tone, “neutral” and “praising” appeals dominated the interactions, reflecting engagement and appreciation for chatbot responses, while “reprimanding” tones were relatively low. These findings highlight the need for tailored chatbot interventions that encourage students to seek AI assistance for a broader and more in-depth range of writing tasks.

Abundant research has tried to understand how games can be designed and used effectively to improve the learning process and to examine the correlations between digital learning games and student motivation, engagement, and knowledge retention. The current study examined the correlation between learning through digital game-based learning (DGBL) and students’ achievements, their sense of involvement, and motivation for learning. Using a quantitative approach, data was drawn from questionnaires and exams in two subjects: literature and language. Participants were 320 male and female students aged 12–14 attending a single middle school participated in the study. The students were randomly divided into three groups, each group was given a unique teaching and learning method. Group 1 studied and practiced using the traditional method (a teacher who teaches in the classroom and worksheets for practice), Group 2 studied with the traditional method but practiced with a digital game and Group 3 learned and practiced using a digital game. The findings showed that the students’ attained significantly higher achievements in the group that was taught traditionally but practiced with a digital game. It was also found that when digital learning games are integrated into teaching and learning, the students’ motivation and involvement in the class increased.

The growing integration of generative artificial intelligence (GenAI) tools in higher education has potential to transform learning experiences. However, empirical research comparing GenAI-supported learning with traditional instruction lags behind these developments. This study addresses this gap through a controlled experiment involving 96 undergraduate computer science students in a Database Management course. Participants experienced either GenAI-supported or traditional instructions while learning the same concept. Data were collected through questionnaires, quizzes, and interviews. Analyses were grounded in self-determination theory (SDT), which posits that effective learning environments support autonomy, competence, and relatedness. Quantitative findings revealed significantly more positive learning experiences with GenAI tools, particularly enhancing autonomy through personalized pacing and increased accessibility. Competence was supported, reflected in shorter study times with no significant achievement differences between approaches. Students performed better on moderately difficult questions using GenAI, indicating that GenAI may bolster conceptual understanding. However, interviews with 11 participants revealed limitations in supporting relatedness. While students appreciated GenAI’s efficiency and availability, they preferred instructor-led sessions for emotional engagement and support with complex problems. This study contributes to the theoretical extension of SDT in technology-mediated learning contexts and offers practical guidance for optimal GenAI integration.