Mid Sweden University

Artificial Intelligence (AI) is reshaping education, learning, and instruction, yet current research in this area is fragmented, often tool-specific, and dominated by short-term perspectives. This article develops a broader research agenda for AI and Education (AI&ED), bringing together Artificial Intelligence in Education (AIED) and AI literacy within an educational ecology framing. Using a collective writing methodology, an expert panel of eleven internationally recognised scholars from various disciplines within computer and learning sciences contributed ten standalone reflections on the challenges, opportunities, and transformations of AI&ED. Two additional leading scholars provided critical commentaries to strengthen the analysis. A thematic analysis of the contributions identifies five main challenges (learning and instructional practices and curricula, access and ethics, assessment and evaluation, research capacity, and stakeholder readiness), five areas of opportunity (enhanced pedagogies, innovation in design and research, support for learning processes, critical skills, and hybrid knowledge), and four transformational themes (AI technologies and the design of education, human-AI interplay, lifelong learning, and organisation of AI&ED research). The article proposes an educational ecology research agenda across macro (policy, research ecosystem, society), meso (curricula, institutions, leadership), and micro (instructors, learners, learning processes) levels. We argue for a future-oriented, critical, and inter- or multidisciplinary approach that recognises AI as a socio-technical assemblage and sustains educational values such as equity, democracy, and human dignity in postdigital societies.

The rapid development of tools and techniques in the field of Generative AI (GenAI) has affected many sectors. One of these sectors is definitely education, where teaching, learning, assessment, curricula and policy document need to be revised and updated. Many research studies also highlight the necessity for teacher professional development regarding Artificial Intelligence in Education (AIED), as AIED is also a field under constant development and will need continuous upskilling during the coming years. There are now teacher training courses in fundamental AIED available, and more are under development. There seems to be a consensus regarding what an introduction course in AIED should comprise, but not regarding which topics continuation courses should follow-up related to continuous lifelong learning. With the heutagogical idea of asking the learners about what to learn, this question was posted to participants in a course on fundamental AIED. In a discussion forum, course participants gave their suggestions and commented on other course participants' postings. Moreover, the forum postings were supplemented with suggestions and comments from email conversations between the authors and course participants. According to the concept of Open Coding, forum postings and email conversations were analysed and divided into the categories of: AI didactics, GenAI tools for teaching, Prompt engineering, Audio generation and Voice cloning, Customisation of AI models, AI and disinformation, Applicable takeaways and AI sustainability and ethics. All of the categories were found to be relevant in a second Axial coding reanalysis. The category Applicable takeaways was found to be the axial category that ties all of the categories together for a meaningful course design. The conclusion is that a continuation course, as in introductory courses on AIED, must contain both theoretical parts with themes such as AI sustainability and ethics, but also concrete applications such as AI didactics to fulfil the aim of Applicable takeaways. Finally, it could be difficult to involve all the categories in just one or two continuation courses. However, as mentioned earlier, AIED should to be seen as continuous lifelong learning.          

In the current Artificial Intelligence (AI) spring, the rapid development affects different areas differently, and the same is true for professional development on AI. In some areas there is a need for a narrow and specialised training on specific AI tools and techniques. Regarding artificial intelligence in education (AIED), several research studies have highlighted the need for a broader professional development that involves all teachers and instructional designers. This study has investigated the design and implementation of a 7.5 ECTS standalone distance course on AIED and its first preliminary evaluation. The course participant group consisted of a high percentage of fulltime working teachers with a clearly higher average age than most courses at a department of education. The aim of the study is to explore the course participants opinions and their feedback in the very first version of this course. With the idea of iterative design science research, the results from this study will be used for input for the updates and redesign of the next course versions. Data were collected in a mix of discussion fora postings, course webinars and the official course evaluation. Findings show that the heutagogical design approach was appreciated by the course participants. Solutions to several assignments could be seen as takeaways for further discussions on how to implement AIED in the course participants daily workplace activities. This also seems to be the case for course participants working in other professions such as journalists, lawyers and in digital media. What was appreciated most was the combination of concrete workshops, reflection essays and group discussions in course webinars. Regarding the assignments it was appreciated that it was clearly stated to what degree AI-tools were allowed in the creation of solutions. Areas of development could be found regarding the relatively high workload, and the problem of organising course participants in communities of practice for group work and the exchange of experiences. The conclusion is that the main course structure with the four sections of 1) Introduction and tool testing, 2) AI in Education, 3) Multimodal AI, and 4) Discussion webinars as knowledge cafés should be maintained, but that all sections should be revised and updated in the next course version.           

In higher education, different disciplines have different prerequisites. Science, Technology, Engineering and Math (STEM) education often requires certain earlier course studies with specified grades in subjects such as mathematics and physics. In humanities, many research studies report on student failure due to insufficient second language skills in English. Fewer studies have reported on the importance of sufficient first language skills to cope with reading and writing in the intensive courses that are frequent in social science and humanities. The aim of this study is to investigate the potential relationship between students’ secondary school grades in their first language, and their learning outcomes in university programmes, given in the same language, at a department of education. The research question that guided this study was: what is the relationship between students’ grades in Swedish at secondary level and their level of completion of five-year university programmes. Data include N=2,583 unique students taking full teacher certification programmes at the university during 2016–2024 and was obtained and examined by SQLs from national databases Swedish Council for Higher Education and Ladok. Cross-mapping of data and basic statistical analysis, including linear multiple regression, was performed in SPSS. Results indicate that above average grade levels were connected with higher expected degree completion. A trend with increasingly higher grades in all admitted students during recent years was also observed. These results may concern educational stakeholders and policymakers that work with educational design and related implementations in university programmes. Implications from this study are further discussed, such as potential consequences of restricting university programmes to certain grade level prerequisites, and considerations to generic skills for degree completion of the students. 

Artificial Intelligence (AI) applications have the potential to transform personal, social, and professional activities, including education. Education spans various sectors: K-12, higher education, lifelong learning, and workplace training. It intersects with other fields that are deeply involved in AI research and study. According to published literature, AI in education encompasses both the use of AI to enhance learning experiences and training on the broad applications of traditional and generative AI. In summary, appropriate education on the application of AI across fields and disciplines is essential.

A Master of Education credit-based seminar course was offered online at a large, publicly-funded university in North America. Participants engaged with scholarly readings, discussions, presentations, and written assignments. Their essays covered topics such as instructor support for and with AI, AI-generated images and audio in universal design for learning, and AI for language learning. At the course's completion, participants reported a clearer vision of AI tool use in the classroom and strategies for overcoming potential disruptions or losses.

In connection with this course, an open, nationwide distance course was offered at a publicly funded university in Sweden: "Artificial Intelligence - Theories and Applications for Education and Work-life," a 7.5 ECTS introductory course. The course comprised four main sections: 1) The history of Artificial Intelligence, 2) Artificial Intelligence in education, 3) Multimodal AI, and 4) Discussion webinars as knowledge cafés. Participant essays covered topics such as guidelines for AI use in different educational contexts, assessment and detection software, ethical aspects of AI-generated images, and a SWOT analysis of AI in education. Preliminary course evaluations indicated that the mix of AI theory and practical applications was appreciated, but group work and the concept of communities of practice were challenging to establish in an online course with many participants working full-time. The needs of adult learners studying at a distance remain the same when using and studying AI.

The interest in introducing artificial intelligence in contexts of learning and instruction is not new. Early attempts to understand the relationship between cognitive aspects of learning and artificial intelligence have been documented as early as in the 1950s. Since then, other aspects of learning and instruction in the context of AI technologies have been investigated. Many of these early tryouts did not succeed as expected and could not be implemented. However, technological leaps in the field of AI during recent decades have led to the emergence of technologies with strong capabilities when it comes to supporting communicative aspects of learning and instruction. In the early 2020s, the emergence of generative AI (Gen AI) technologies resulted in a renewed interest in these issues. Chatbots and virtual assistants entered the scene at all levels of educational systems and are widely used in non-institutional settings as well. Recent tryouts show promising results in terms of the capabilities of Gen AI technology when it comes to supporting learning and instruction in a lifelong and lifewide perspective, all the way from contexts of formal instruction in early childhood to informal learning at work and in other settings. These studies have been concerned with issues of how Gen AI technologies can support micro-level, processual aspects of learning and instruction in classroom settings, as well as their potential for addressing more macro and meso-level issues concerning institutional aspects of the functioning and organisation of educational systems.This symposium discusses some of the many issues of learning and instruction that are raised in relation to the recent developments of Gen AI. Four papers are presented by researchers collaborating in an EARLI Centre for Excellence in Research (ECER), AI in Learning and Instruction: Challenges, Opportunities, Transformations (AILI). The first paper by Justin Edwards, Márta Sobocinski, Joni Lämsä and Sanna Järvelä, Aligning with AI - Lexical Alignment Between Collaborative Learners and an AI Mediator, focuses on a tryout in a 7th-grade physical classroom setting. It takes a micro perspective and analyses the lexical alignment between AI agents and collaborative aspects of learning. Also, the second paper, Teacher CoPilot: AI-based Training and Support System in Teacher Education written by Sabine Seufert, explores Gen AI from a micro-level perspective, this time in a higher education setting. It analyses a tryout where a Gen AI teacher copilot intervened to afford the development of higher education teachers' technological, pedagogical and content-related competencies, so-called TPACK. The third paper by Marcus Sundgren, Susanne Sahlin, Rebecca Marrone, Maarten de Laat and Jimmy Jaldemark explores the institutional implications of Gen AI by focusing on issues of the organisational readiness of school principals for adopting Gen AI in their everyday practices. As is the case in the study by Seufert above, the final paper, written by Ylva Lindberg and Anders Buch, focuses on higher education institutions (HEI) and explores how HEIs respond to the requirements of providing teachers and students with guidelines for the future in the heavily contested area of what AI implies for education and learning.

To make higher education accessible for all students and to enable inclusive learning, teachers face high demands in pedagogic knowledge and skills. This assumes knowledge of students with neuropsychiatric disorders (NPDs) as well as specific skills on how to implement suitable pedagogical adaptations. Applicable pedagogic adaptations to this growing student group have also been shown to be beneficial for larger general student populations. However, teachers often indicate difficulties finding and executing adequate pedagogical approaches. There are also knowledge gaps regarding how teachers adapt and accommodate their education and what kind of support they need. The study was designed in two phases: in the first phase the aims were to explore (a) teachers’ knowledge of students with NPDs, and (b) teachers' need of support to implement suitable pedagogical adaptations. The aim of the second phase is to develop a digital technological support system for teachers to be used forhands-on inclusive learning skills. This study was conducted with an action research approach where two of the authors also have been teachers in some of the investigated courses. A questionnaire was constructed and distributed to teachers within various subjects at a Swedish university, targeting their experiences and needs ineducational practice with students with NPDs. The questionnaire involved a combination of background items and open-ended items. Experiences and needs were analysed with reflexive thematic analysis. The questionnaire was the first step in a process of developing a digital technological support system for teachers, based on specific gaps of knowledge and their need for support. 85 out of 260 teachers responded to the survey; 68 % of respondents were women. 42 % were seniorlecturing staff, 49 % junior staff and 9% PhD students. 57% reported that they had not sufficient knowledge about students with NPDs. Teachers described various pedagogical knowledge for accommodating students with NPDs, including universal, individual and relational strategies. Examples were to increase clarity and predictability in all educational efforts, being prepared to flexibly adapt to student needs including increasing teacher availability and strengthening the relation by creating trust and safe communication. Teachers also described their own needs of in-depth knowledge, hands-on guidance, expert advice, and common solutions/discussions with colleagues. They also saw the need for improved organisational structures and support. The findings indicate that despite university teachers being aware of different pedagogical strategies, they still need to develop their knowledge. The knowledge gained from this study will be used as the foundation for development of a digital technological support system for teachers, which can be motivated from three perspectives: to strengthen teachers’ knowledge and skills; to strengthen students’ opportunities to participate in higher education; and to enable the organisation to evaluate and reassure their commitments regulated by laws or regulations. Future research in line with this study may be to implement an artificial intelligence (AI) tool in the digital technological support system.

During the last years there have been a rapid development of a plethora of artificial intelligence (AI) tools, including generative AI (GenAI) models that challenges the existing teaching and learning design in higher education. The current AI hype might be followed by a new AI winter, but the impact on teaching and learning in higher education will be permanent (Pons, 2023; Whitham et al., 2023). Several studies have pointed out the importance of teacher professional development in the field of AI in education (AIED) (Escalona-Márquez et al., 2024; Mouta et al., 2024). A professional development should involve practical, theoretical as well as ethical aspects of AIED. This study was conducted as a part of the initial phase of the project FAITH (Frontline Application of AI and Technology-enhanced Learning for Transforming Higher Education). This is a pedagogical development project that aims to develop institutional teaching development in higher education programs. With the use of GenAI and technology-enhanced learning, teachers in higher education should revise and develop their educational programmes to involve GenAI tools in teaching and learning activities. The project will be implemented during 2024-2026 (Jaldemark et al., 2024). The aim of the study is to introduce, analyse and discuss some GenAI tools for teaching. Data was collected by a web survey at a webinar workshop with 39 participants (36 student participants + 3 course teachers) that was a part of a course on AI for education and work-life. Before answering the questions participants tested AI tools. Questionnaire answers (n=19) were analysed and categorised in a directed content analysis. The theoretical lens for the study was the Self-determination theory (SDT) as outlined by Ryan and Deci (2024). STD postulates that there are three basic psychological needs that must be satisfied for people to experience wellness, ongoing growth and integrity. Among 11the survey participants, common age group were 45-54 (n=9), followed by 25-34 (n=4) and 35-44 (n=3). Their occupations covered work in, for example: government agencies, schools and universities, industry, project management, marketing, and HR. Results of the preliminary analysis highlights that AI can support language development and analysis, idea generation, more efficient work, and be an assistant in schools. However, answers to the survey also highlight that AI have several limitations, such as lack of emotional understanding and critical and creative thinking. Participants highlight that AI can strengthen and develop human competences, but also, that it is necessary to develop competences within AI for future society. On the negative side it is noted that too much emphasis on AI can make us downgrade human thinking in the human-AI-collaboration, potential loss of human jobs, and that all may not receive adequate competence development in schools and industry.

The current wave of digitalization and Artificial intelligence (AI) has highlighted the need for adaptation of AI techniques in various sectors, and particularly in education. Preparing students for a digitalized world with AI-tools and techniques is now imperative, with an extra strong need in programmes on teacher training. Universities must incorporate AI-related theories and practices into their curricula, to equip current and future educators with the necessary knowledge and skills to update teaching and learning activities. This study aims to examine, analyse and discuss the curricula of eight preschool education programmes at some selected universities. The focus of a document analysis has been on how these programmes incorporate concepts related to AI and digitalization. The research question that guided this study was: "How are digitalization and AI-related theories and methods integrated into the preschool teacher education curricula at Swedish universities, and what are the perceived challenges and opportunities in integrating these technologies?"

The study approach was a document analysis that was carried out in the steps of:

1) familiarization,

2) identifying a thematic framework,

3) indexing,

4) charting, and finally

5) interpretation.

This was combined with a deductive thematic analysis with a focus on new emerging sub-themes of the preset themes. Findings show that none of the curricula mentions the word artificial intelligence or the abbreviation AI. However, themes from the analysis that have a relationship to AI, and probably in some form will involve AI in the future were: 'Digitalization of Higher Education', 'Digital Tools for Education', and 'Science, Math, and Digital Expression'. Authors recommendation is to open discussion fora about what kind of education we want our preschool teachers to receive to prepare children for a future world where AI is a ubiquitous part of everyday life. An interesting idea for future research would be to interview participants in the preschool education programmes to get their opinions on the existing curricula.

As Artificial Intelligence (AI) reshapes education, professional development (PD) must go beyond tool training to foster critical, meaningful integration. Initial PD should introduce AI’s uses and challenges, but also address the impact on teaching and learning. This paper explores and reflects upon Phase II of the FAITH project, a transatlantic design-based initiative developing an AI and Education (AI&ED) model for higher education. Effective AI pedagogy is grounded in socially constructed, hands-on experiences where educators design lessons, generate content, and critically assess AI outputs. Such approaches build confidence, competence, and prevent mechanical adoption. Leadership and policy must further support a dual PD strategy: immediate classroom applications alongside preparation for broader societal shifts. Early FAITH findings show introductory courses spark essential dialogue, but PD must remain dynamic, ethical, and intentional. Phase II combines theoretical exploration (e.g., sustainability, ethics) with context-relevant practice. Ultimately, AI&ED should be understood as a lifelong professional learning journey.