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This article examines how different configurations of artificial intelligence (AI) in education may reshape teachers’ opportunities to experience teaching as meaningful work. Using a scenario methodology, we develop three plausible scenarios grounded in existing educational AI: Labor-Replacing Classrooms, where AI tutors displace core instructional tasks and teachers are redeployed into surveillance and exception-handling; AI-Managed Teaching, where teachers remain central but are guided and evaluated through dashboards, nudges, and performance indicators; and Human–AI Teaming, where AI is implemented as a teacher-governed, co-designed support intended to augment professional judgment. Drawing on work-design theory and scholarship on meaningful work, we assess each scenario against five dimensions: task integrity, skill cultivation and use, task significance, autonomy, and belongingness. The analysis indicates that labor substitution and the pervasive use of algorithmic surveillance threaten the integrity of pedagogical processes, potentially constraining professional autonomy and shifting the focus of teaching toward metrics and outputs that are easily processed by machines. While human–AI collaboration has the potential to enhance agency and foster collegial inquiry, it remains fragile and could lead to overdependence on AI systems if governance structures and workflows are not kept under scrutiny. Overall, the trajectory of AI in education depends on policy, on which technologies are adopted, and on broader institutional choices that can either erode or safeguard meaningful teaching.

This article examines the psychosocial consequences of meritocratic education systems, with particular focus on students who are positioned as having failed within these competitive and ostensibly fair frameworks. While meritocracy promises equal opportunity, extensive research shows that educational achievement is strongly shaped by socioeconomic background and by socially and culturally recognized codes and resources that systematically advantage some students over others. As a result, meritocratic selection often reproduces inequality while framing unequal outcomes as deserved. The article synthesizes evidence on how meritocratic ideology supports system justification, leading even disadvantaged students to internalize beliefs that school outcomes are fair. This contributes to self-blame, stigma, and psychological distress when performance falls short. The analysis further shows how educational failure carries long-term economic and social penalties, reinforcing class divisions and eroding social cohesion. The article outlines alternative educational logics that may mitigate these harms, including redistributive support, plural pathways, second-chance opportunities, broader conceptions of ability, recognition of diverse contributions, and cooperative learning. Together, these approaches offer ways to reduce the psychosocial burden of failure and support more equitable educational futures.

En anledning till att AI anses ha stor potential är att den skulle kunna förbättra produktiviteten i tjänsteyrken. Baumoleffekten beskriver fenomenet att vissa sektorer, såsom högre utbildning, upplever stigande lönekostnader trots relativt statisk produktivitet och därmed blir dyrare över tid. Denna effekt är enligt forskning den viktigaste faktorn bakom ökade kostnader för offentlig sektor under efterkrigstiden. Sedan 1990-talet hanteras detta i statlig sektor genom det så kallade produktivitetsavdraget, vilket innebär att statliga verksamheter inte kompenseras fullt ut för ökade lönekostnader. Detta avdrag påverkar i högre utsträckning verksamheter som inte är avgiftsfinansierade, till vilka högre utbildning hör. Produktivitetsavdraget är omdiskuterat bland annat eftersom det förutsätter att verksamheten som bedrivs inom högre utbildning faktiskt kan effektiviseras på ett sätt som kompenserar för denna anslagsminskning.

Det finns förhoppningar om att AI kan bidra till en automatisering, det vill säga användning av olika teknologier för att utföra uppgifter som traditionellt kräver mänsklig insats, av vissa läraruppgifter inom högre utbildning på ett sätt som skulle kunna motverka Baumoleffekten. I ljuset av detta vill vi utforska möjligheten och konsekvenserna av automatisering i akademisk undervisning som en lösning på denna produktivitets- och kostnadsutmaning. Syftet med vårt bidrag är att diskutera relationen mellan automatisering med hjälp av AI, produktivitetsutveckling och kvalitetsuppfyllnad inom högre utbildning. Med utgångspunkt i tidigare forskning om produktivitetsutveckling inom högre utbildning och relationen till kvalitet, diskuterar vi AI:s potential att bidra till förbättringar eller försämringar.

Förväntade resultat pekar på att kvalitetskriterierna måste väljas noggrant när man ska diskutera produktivitet i högre utbildning. Hur man väljer mellan olika kriterier är avgörande för i vilken mån AI kan skapa en produktivitetsökning som inte samtidigt riskerar att utarma den mellanmänskliga interaktion som antas ligga till grund för många av den högre utbildningens målsättningar. Vi argumenterar för en modell där AI används som ett komplement till lärare. Vår förhoppning är att detta stödjer en högkvalitativ högre utbildning som kan hantera effektiviseringskravet på ett konstruktivt sätt.

In this presentation, I will explore two broad and simplified types of situations in which emotions could be understood to play a role in men’s pro-feminist and anti-feminist engagement. In the first situation, the men come in contact with feminist narratives that elicit either positive or negative emotions and motivate pro-feminist and anti-feminist engagement. In the second situation, the men encounter non-feminist narratives about feminism or gender equality which produce positive or negative emotion and motivate pro-feminist or anti-feminist engagement. In both situations and regarding pro-feminist as well as anti-feminist men, negative emotions (such as anger, guilt, fear and sadness) are more frequently evoked than positive emotions (such as curiosity and love). I will illustrate the different situations in which emotions and narratives motivate men’s pro-feminist and anti-feminist engagement through a reanalysis of empirical data (interviews and texts from both traditional and social media) collected during the work on my thesis.

The overarching aim of the thesis is to describe and analyze men’s pro- and antifeminist engagement. Men’s approach to feminism is assumed to be created in the interaction of men’s intentions and a sociocultural structure that conditions human action. Men want something that they perceive that feminism enables or constrains, after which they engage for or against it. The men’s views on feminism were extracted from interviews, social media, books, and newspaper articles. A host of philosophical assumptions, general analytical theories, and specific analytical tools were used in processing the empirical data. In the first part of the findings, profeminist men are understood to view feminism as a force that enables them to accomplish important projects in their lives. They express a wish to improve both women’s and men’s lives through the practice of changing men and masculinity. Above all, their efforts focus on changing themselves. In the second part of the findings, antifeminist men are understood to experience feminism as a force that constrains them in their lives. They oppose feminism on several issues: women’s subordinated position in the gender order, the social construction of gender, and men’s violence towards women. They fear that state feminism is pushing a hatred of men that threatens individual men, nuclear families, and Western civilization. The third part of the findings consists of a model of men’s approach to feminism. In the model, men’s differing approaches hinges on whether they believe that feminism enables or constrains them and other people in their quest to satisfy basic human needs. Their understandings of if and how the needs are being met are central to how men approach feminism. In this understanding, the men are influenced by their respective ideological beliefs on how the gender order should be organized. The thesis ends with two methodological musings. The first revolves around the author’s development of a deeper understanding of the antifeminist worldview and how that came to rub against his profeminist way of viewing the world. The second reflects on researchers’ understandable but counterproductive tendency of trying to protect their research from all criticism.