Work on developing team models for use in adaptive systems generally and intelligent tutoring systems more specifically has largely focused on the task skills or learning efficacy of teams working on short-term projects in highly-controlled virtual environments. In this work, we report on the development of a balanced team model that takes into account task skills, teamwork behaviours and team workflow that has been empirically evaluated via an uncontrolled real-world long-term pilot study of student software engineering teams. We also discuss the use of the the J4.8 machine learning algorithm with our team model in the construction of a team performance prediction system.

Work on developing team models for use in adaptive systems generally and intelligent tutoring systems more specifically has largely focused on the task skills or learning efficacy of teams working on short-term projects in highly-controlled virtual environments. In this work, we report on the development of a balanced team model that takes into account task skills, teamwork behaviours and team workflow that has been empirically evaluated via an uncontrolled real-world long-term pilot study of student software engineering teams. We also discuss the use of the the J4.8 machine learning algorithm with our team model in the construction of a team performance prediction system.

Dynamiska AI-system tar får allt mer utrymme i skolans värld. Det kommer påverka utbildning, undervisning och inlärning och inte minst lärares och studenters roller. Syftet med denna studie är därför att undersöka hur lärares och elevers roller, enligt de trender som framkommer i konferenserna AIED 2021 och LAK21, påverkas av AI-system i skolan. Materialet i denna studie utgörs av konferensartiklarna som presenterades under de två konferenser 2021 som iscensattes av International Society of Artificial Intelligence in Education (IAED) och Society for Learning Analytics (SoLAR). I sin forskning fokuserar båda forskningssällskapen användandet av AI i skolan. Båda ser sig som ledande inom sina fält. Av sammanlagt 187 har 130 uppfyllt de ställda kriterierna för att inkluderas i denna litteraturöversikt. Artiklarna har kategoriserats utifrån vilka AI-system som använts och deras påverkan på lärar- respektive studentrollen. Resultatet som framkommer visa på en mer passiv lärarroll och en mer aktiv studentroll. Förklaringen till detta är att AI-systemen förutser studentresultat, övervakar, ger feedback och pushar studenten till handling. I det bästa av scenarion kan däremot läraren ses som en partner till AI-systemen. Konferensartiklarna skriver inte mycket om lärare, för nästan allt fokus ligger på studenten. Det innebär att det är upp till systemen att ta plats på lärarnas bekostnad. Denna utveckling belyser vikten av att lärare och studenter får ta del av konsekvenserna av införandet av AI-system i skolan. / Dynamic AI-system are moving fast into the world of schools. This will affect education, teaching and learning and not at least the roles of teachers and students. That is why the aim of this study is to examine how teachers and students roles, according to the trends that emerge in the AIED 2021 Conference and the LAK21 Conference, are affected by AI-system in school. The starting point for this thesis are the studies that are presented during the two major conferences that are being held by the International Society of Artificial Intelligence in Education (IAED) and the Society for Learning Analytics (SoLAR) in 2021. In their research they both are focusing the use of AI in education. The societies are asking to be recognized as leading in their fields. Out of 187 articles published during the conferences 130 have fulfilled the criteria to qualify in this literature review. The articles have been categorified due to the AI-system that has been used and their influence on the role of the teacher and the student. The result demonstrates the roles of a more passive teacher and a more active student. The explanation for this is that the AI-system supervise, give feedback and push students into action. In the best of scenarios, the teacher can be seen as a equal partner with the systems. However, not much is being written about the teachers, it´s all about the students. Since the teacher is out of focus, it´s up to the system to fill that vacuum. This development makes it important that the consequences of the introduction of AI-systems in education are known to the concerned parties – the teachers and the students. / <p>Godkännande datum: 2022-06-03</p>

Den här studiens syfte är att undersöka hur gymnasielärare i företagsekonomi upplever arbetet med AI, vilka möjligheter och utmaningar de identifierar samt vilket stöd de efterfrågar. Studien har genomförts via kvalitativa intervjuer med fem gymnasielärare i företagsekonomi. Lärarna representerar olika kön, olika lång erfarenhet av läraryrket samt tre olika gymnasieskolor i två olika regioner i Sverige. Studien visar att lärarna som kollektiv ger uttryck för en tro på en framtid där AI-verktygen kommer att vara en naturlig del i företagsekonomiutbildningen till följd av utvecklingen i näringslivet och samhället i stort där AI får en allt mer central roll. De intervjuade lärarna ser möjligheter i form av effektivisering och stöd i sitt eget arbete och identifierar även områden inom företagsekonomiämnet där AI kan bidra till att ge eleverna en ökad förståelse. Huvudsakliga utmaningar som lärarna ringar in innefattar att upptäcka och förhindra AI-fusk, att bedöma elevers prestationer samt stötta eleverna i att utveckla källmedvetenhet och kritiskt tänkande. De ser en utveckling där deras arbete förändras till att handla mer om validering av information och kunskaper. Samtidigt signalerar de att många frågetecken finns kring hur arbetet kommer att påverkas i förhållande till styrdokumenten och efterfrågar stöd i form av kompetensutveckling inom AI-området samt riktlinjer från Skolverket.

This dissertation investigates computational models and methods to improve collaboration skills among students. The study targets pair programming, a popular collaborative learning practice in computer science education. This research led to the first machine learning models capable of detecting micromanagement, exclusive language, and other types of collaborative talk during pair programming. The investigation of computational models led to a novel method for adapting pretrained language models by first training them with a multi-task learning objective. I performed computational linguistic analysis of the types of interactions commonly seen in pair programming and obtained computationally tractable features to classify collaborative talk. In addition, I evaluated a novel metric utilized in evaluating the models in this dissertation. This metric is applicable in the areas of affective systems, formative feedback systems and the broader field of computer science. Lastly, I present a computational method, CollabAssist, for providing real-time feedback to improve collaboration. The empirical evaluation of CollabAssist demonstrated a statistically significant reduction in micromanagement during pair programming. Overall, this dissertation contributes to the development of better collaborative learning practices and facilitates greater student learning gains thereby improving students' computer science skills.

Mentoring is a highly personal and individual process, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals. The emerging use of AI in mentoring processes in higher education not only necessitates the adherence to applicable laws and regulations (e.g., relating to data protection and nondiscrimination) but further requires a thorough understanding of ethical norms, guidelines, and unresolved issues (e.g., integrity of data, safety, and security of systems, and confidentiality, avoiding bias, insuring trust in and transparency of algorithms). Mentoring in Higher Education requires one of the highest degrees of trust, openness, and social–emotional support, as much is at the stake for mentees, especially their academic attainment, career options, and future life choices. However, ethical compromises seem to be common when digital systems are introduced, and the underlying ethical questions in AI-supported mentoring are still insufficiently addressed in research, development, and application. One of the challenges is to strive for privacy and data economy on the one hand, while Big Data is the prerequisite of AI-supported environments on the other hand. How can ethical norms and general guidelines of AIED be respected in complex digital mentoring processes? This article strives to start a discourse on the relevant ethical questions and in this way raise awareness for the ethical development and use of future data-driven, AI-supported mentoring environments in higher education.

This study focusses on artificial intelligence (AI) in education, AIEd, and on how AI language models, such as ChatGPT, impact teachers' ability to develop the pupils' skills and knowledge, specifically at an upper secondary school. Three language teachers at a Swedish upper secondary school have been interviewed with the purpose of unveil new insights within the purpose of this piece of research which is to contribute with an understanding of how to develop pupils' skills and knowledge through successfully integrating artificial intelligence in education (AIEd) in an upper secondary school. After defining the problem analysis and discussion for this thesis my conclusion is that there are several recent studies performed for K-12 schools but not conducted enough research for specifically upper secondary schools in relation to AI, since the introduction of ChatGPT and other large language models (LLMs). Therefore, the ambition with this thesis is to shrink the knowledge-gap in answering the research question and fullfilling the purpose of this thesis. The analysis in this thesis is a semantic thematic analysis and to facilitate reading the empirics and analysis parts every theme is included in the empirics so the connection to that latter analysis is displayed in the empirics and in this way constitutes a bridging between the empirics to the latter analysis. I hope you find the study worthwhile reading and if you are a teacher, it will bring some support in the choices you are facing in the era of LLMs in an upper secondary school.

Artificiell intelligens (AI) utvecklas inom bland annat utbildning och teknikens roll inom lärosäten diskuteras i stor utsträckning. AI och dess förmåga besitter möjlighet att leda till innovationer inom lärande och om AI används till sin fulla potential har tekniken förmåga att förstärka människans intelligens. Tidigare forskning fokuserar dock till stor del på tekniska aspekter av AI inom utbildning och det existerar en avsaknad av kunskap rörande hur tekniken faktiskt upplevs och används i praktiken av studenter. Det är nödvändigt att vidare forskning skiftar fokus och undersöker hur de egenskaper AI besitter skiljer sig från traditionella digitala verktyg och hur tekniken kan skapa värde för studenter inom studier. Denna studie ämnar därför att genom en kvalitativ metodansats undersöka hur AI som studieverktyg kan främja studenters studieteknik. Studien bestod av semistrukturerade intervjuer med studenter som testat eller använder AI inom sina studier och resulterade i sju designutmaningar. Designutmaningarna belyser avgörande aspekter av hur AI kan främja studenters studieteknik utifrån de upplevelser och behov studenter uttrycker. / Artificial intelligence (AI) is being developed in various fields, including education, and the role of technology in academic institutions is being widely discussed. AI and its capabilities have the potential to lead to innovations in learning and if AI is used to its full potential, the technology can augment human intelligence. Previous research has focused on the technical aspects of AI in education, and little is known about how the technology is experienced and used in practice by students. It is necessary that further research shifts the focus and investigate how the unique characteristics of AI differ from traditional digital tools and how the technology can create value for students in their studies. This study therefore aims to investigate, through a qualitative methodological approach, how AI as a study tool can promote students' study techniques. The study consisted of semi-structured interviews with students who tested or use AI within their studies and resulted in seven design challenges. The design challenges highlight critical aspects of how AI can promote students' study techniques based on the experiences and needs expressed by students.

Denna undersökning har syftat till att undersöka hur AI anses kunna påverka undervisningen i skolan och samhällskunskapsämnet. Studien baseras på den diskurs som råder inom ämnet. Undersökningen baseras på material i form av tidningsartiklar, en intervju med en forskare och en fokusgruppsintervju. Materialet har analyserats genom en kvantitativ respektive kvalitativ innehållsanalys. Undersökningen utgår från uppfattningen att samhällskunskapsundervisningen bör sträva efter att utveckla så kallad AI-literacy hos eleverna. Resultatet bekräftade inte att detta genomförs i undervisningen. Områden som undervisning, fusk, examination och bedömning behandlades i större utsträckning än AI-literacy, AIED (AI in education) och AI&amp;ED (AI and education).  Resultatet antyder att AI-utvecklingen kommer leda till nya examinationsformer och möjligen AI-assistenter som kompletterar lärare. Samhällskunskap som undervisningsämne omnämndes knappt i materialet även om vissa områden som kan relateras till samhällskunskap förekom som källkritik och demokrati. AI-literacy relaterat till samhällskunskap förekom några enstaka gånger. Detta resultat påvisar viken av att utveckla AI:s roll i samhällskunskapsundervinsingen för att möjliggöra säkra studier av ämnet i skolan, samt vikten av att fortsätta forska om AI och utbildning och AI och samhällskunskap. / This survey aims to investigate how AI is considered to influence teaching in school and the social studies subject. The survey is based on the discourse on the subject of AI and education. The survey is based on material from online news articles, an interview with a researcher and a focus group interview. The material has been analyzed through quantitative and qualitative content analysis methods. The survey is based on a theory that claims that social studies teaching should strive to develop students so-called AI-literacy. The survey results did not confirm this theory. Categories such as teaching, cheating, examination and assessment were covered in the material to a greater extent than AI literacy, AIED (AI in education) and AI&amp;ED (AI and education). The results suggests that the development in AI may lead to new forms of examination and possibly AI assistants as a supplement to teachers. Social studies as a teaching subject was barely mentioned in the material, although certain areas that can be related to social studies appeared such as source criticism and democracy. AI literacy related to social studies appeared a few times. This result demonstrates the importance of further research about AI and social studies education to and to enable safe methods to study the subject in schools. It also emphasizes the importance of continued research on AI and education as well as AI and social studies.