This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72% of the studies focused on students, 17% instructors, and 11% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT.

English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. However, English learners face many challenges in gaining English language skills. Extant studies show that AI has affordances to support in English language teaching and learning ELT/L. This study answers the call to examine specific challenges and affordances for using AI in ELT/L. A systematic review method was used with PRISMA principles to identify 42 studies. Findings reveal the geographical locations of studies, learner ages and years of study. Grounded coding was then used to identify affordances of the use of AI in ELT/L in the areas of speaking, writing, reading, pedagogy and self‐regulation. AI in ELT/L challenges uncovered were technology breakdowns, limited capabilities, fear and standardising language. Policymakers, funders, practitioners and educational leaders can use the information provided in this study to gain a holistic understanding of the current trend in the use of AI in ELT/L, and practical implications are provided to guide future use of AI. Practitioner notes: What is already known about this topic English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. Empirical evidence shows that pupils can often face difficulties when learning English, with challenges such as irregularity in English spelling. AI has supported language teaching and learning with studies showing that AI can support language‐specific skills. What this paper adds Provides the scholarly community with a unique systematic review in the use of AI in ELT/L across learner levels. Identifies affordances of AI in ELT/L in speaking, writing, reading, pedagogy and self‐regulation. Identifies challenges of AI in ELT/L in technology breakdowns, limited capabilities, fear and standardising language. Provides researchers with a review of the field with identification of gaps and future research opportunities. Implications for practice and/or policy Provides practical implications from the findings for educators, policy makers and program designers. Highlights the gaps in academic knowledge as a lack in the use of AI for assessment in ELT /L.

Open Praxis is a peer-reviewed open access scholarly journal focusing on research and innovation in open, distance, and flexible education. It is published by International Council for Open and Distance Education - ICDE Web of Science (ESCI) Impact Factor for 2023 is 0,9; ranked 457 out of 759 in Education and Educational Sciences in Q3. Scopus Cite Score is 3.5; ranked 453 out of 1543 in Education category in Q2 and 72 out of 2801 in Library and Information Sciences category in Q2. Facts and Figures: 2024 article acceptance rate is 33%, rejection rate is 67% (13% desk rejections - 54% rejections after review). While the number of days for acceptance is 44, the number of days for rejection is 16.

Artificial Intelligence in Education (AIEd) has experienced a rapid rise in the past decade. This systematic review is the first examining the use of AIEd in K-12 including 169 extant studies from 2011 to 2021. This study provides contextual information from the research, such as the educational disciplines, educational levels, research purposes, methodologies, year published and who the AI was intended to support. The grounded coding revealed affordances fit into three main themes of AIEd connecting to pedagogies (e.g., gaming, personalization), administration (e.g., diagnostic tools), and subject content. Challenges in AIEd K-12 included issues toward negative perceptions, lack of student and teacher technology skills, ethical concerns, and issues directly with the ease of use and design of the AI tools.