Artificial intelligence (AI) in the public sector is a debated topic that has the potential to enhance performance. The public sector is complex and characterized by bureaucratic challenges, posing significant procedural and financial difficulties. Using AI systems can substantially enhance the efficiency of administrative processes and the quality of services provided to citizens. However, the use of AI, in general and in the public sector, has mainly been focused on ethical issues such as trust, bias, or surveillance. One crucial factor directly affecting the efficient and ethical use of AI systems is employee competence. Employees' competence is, however, one of the least explored areas when it comes to working with AI systems, especially in the public sector. This study addresses this gap by examining the perceived necessary competences of employees within the Swedish public sector utilizing a qualitative research method and semi-structured interviews. The findings reveal three overarching areas of competences: 1) systemic competence, encompassing AI literacy and general knowledge; 2) Particular competence relating to expertise where the AI is being used; 3) Contextual competence involving data governance, legal implications, and personal data protection.

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&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&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.

Denna kvasiexperimentella studie syftade till att undersöka hur en lektionsserie kan struktureras och implementeras med mål att utveckla gymnasieelevers förmåga att använda sig av generativ artificiell intelligens som ett pedagogiskt verktyg. För att möta detta syfte genomfördes tre lektioner om artificiell intelligens, maskininlärning, neurala nätverk och stora språkmodeller med fokus på utveckling av teknisk kunskap och praktiska färdigheter med inslag av etik och kritik. Valet av dessa teman grundades i ett tidigare etablerat ramverk för undervisning inom AIläskunnighet. Vidare teman tas dessa teman upp som del av teknikprogrammet och den kommande AI-kursen enligt Skolverkets förslag. Lektionsseriens påverkan kvantifierades med hjälp av två enkäter – en innan och en efter genomförandet av lektionsserien. Lektionsserien presenterades för två gymnasieklasser vilka bestod av totalt ungefär 50 elever. Urvalet av gymnasieklasserna grundades i deras anslutning till den uppdragsgivande läraren. Vidare valdes respondenterna till enkäten utifrån de elever som fysiskt deltog på den första och sista lektionen och frivilligt valde att svara på enkäten. Dessutom intervjuades fyra tekniklärare för att bättre anpassa lektionsinnehållet till målgruppen. Analysen av svarsfrekvensen till enkätfrågorna visade att lektionsserien hade en statistiskt signifikant påverkan på elevernas tekniska kunskaper, men dess påverkan på elevernas praktiska färdigheter var i stort statistiskt insignifikant. Samtidigt påvisade frekvensanalysen att gymnasieeleverna i regel överskattade sin förmåga att kritiskt granska datorgenererad text och var i stort omedvetna om relevanta etiska frågeställningar. Explorativa faktoranalysen visade att det existerar åtminstone två typer av elever. En elevgrupp av okänd storlek använder sig av stora språkmodeller för att accelerera sina studier genom att lösa problem de annars inte kunde lösa. I detta fall har artificiell intelligens en multiplicerande effekt på elevernas produktivitet. En annan elevgrupp av okänd storlek har i stället som mål att förbättra sina skolresultat genom att använda sig av stora språkmodeller för att lösa deras problem åt dem. Samtidigt överskattar dessa elever sin förmåga att granska datorgenererad text. I detta fall har artificiell intelligens en dämpande effekt på elevernas lärande. Studiens slutsats är att det i dagsläget finns behov för undervisning av gymnasieelever på teknikprogrammet om artificiell intelligens. Detta utrymme kan i stort uppfyllas av en tre lektioner lång lektionsserie. Dock erkänner studien att det finns ytterligare utrymme för praktiska moment där läraren handleder eleverna i deras användning av verktyg såsom ChatGPT. Vidare finns det utrymme för kontinuerligt arbete med kritik och etik, möjligtvis som del av de tidigare nämnda praktiska momenten. / This quais-experimental study aimed to investigate how a series of lessons could be structured and implemented with the goal of developing secondary level students’ ability to use generative artificial intelligence as an educational tool. To meet this goal three lessons on artificial intelligence, machine learning, neural networks, and large language models were conducted, focusing on the development of technical knowledge and practical skills with the inclusion of ethics and critical thinking. The choice of these topics was based on a previously established framework for AI-literacy education. Further, these topics are brought up as part of the Swedish upper secondary school technology programme as well as the upcoming AI-course as per the proposal made by the Swedish Agency for Education. The impact of the lesson series was quantified using two form surveys – one before and one after the implementation of the lesson series. The lesson series was presented to two student classes totalling roughly 50 students. The selection of student classes were based on their affiliation with the assigning teacher. Further, the survey respondents were sampled from the students who physically attended the first and last lesson and voluntarily elected to respond. Additionally, four technology teachers were interviewed to better adapt the teaching material to the student demographic. Response analysis showed that the lesson series had a statistically significant impact on students’ technical knowledge, but its impact on students’ practical skills was largely statistically insignificant. At the same time, the frequency analysis indicated that students generally overestimated their ability to critically evaluate computer-generated text and were largely unaware of relevant ethical issues. Exploratory factor analysis had shown that there exist at least two types of students. A student group of unknown size use large language models to accelerate their studies through solving problems they could not otherwise solve. In this case, artificial intelligence has a multiplying effect on the students’ productivity. Another group of students of unknown size instead use large language models to solve their problems for them with the goal of improving their academic performance. At the same time, these students overestimate their ability to evaluate computer-generated text critically. In this case, artificial intelligence has a dampening effect on the students’ learning. The study concludes that there is a need for teaching secondary level students from the technology programme about artificial intelligence. This space can largely be fulfilled by a series of three lessons. However, the study acknowledges that there remains room for practical activities where the teacher guides students in their use of tools such as ChatGPT. Furthermore, there is room for ongoing work on critical thinking and ethics, possibly as part of the aforementioned practical activities.

Abstract: This thesis investigates the capacity of individuals to detect AI-generated text, and the indicators that enable them to do so. This inquiry is situated in the broader theoretical context of digital capital, the digitization of society, deep mediatization, and AI literacy. Using a quantitative correlation approach, the study tested participants’ accuracy in detecting AI content, and shared factors between participants with high scores on this task. Participants were assessed on a number of self-reported demographic, digital capital, and digital society-based benchmarks in conjunction with AI detection accuracy. The study employed a mix of statistical methods, including logistic regression and point-biserial correlation matrices. However, only a few specific questions within the digital capital and digital society framework had a statistically significant impact on a participant being in the high-accuracy group, and these correlations were weak. Furthermore, two aspects of digital capital actually had a negative effect on the odds of scoring high on the text detection task.  The findings reveal that there is room for more research into what indicators influence human AI detection capabilities, and whether these skills are learnable or inherent to certain individuals. Moreover, the research highlights the necessity of fostering AI literacy, particularly if these capabilities improve human AI detection. While AI systems can ‘catch’ AI-generated text, their efficacy is mixed, and producers of AI text and evaluators are constantly locked in a game of cat-and-mouse, using evolving AI to recognize evolving AI. Thus, human skills are pivotal, lest we become even more dependent on technology in our deeply mediatized society.