The thesis examines how critical AI literacy can come to be in mathematics education. This is explored in relation to steering documents, pre-service teacher education, and classroom context. The results indicate that mathematics education with critical and ethical perspectives on AI technology is didactically possible, in the sense that teacher education and mathematics lessons can be shaped around such goals. However, the results also indicate that the traditions of mathematics education and expectations about what mathematics and mathematics education are and should be, are characterized by notions that contrast with such learning about AI technology.

Mathematical AI-models are a force in socio-ecological crises that mathematics education cannot ignore. Through a dialogue, we explore how the capacity and procedures in AI-mathematical models alter the power of mathematical modeling in society. We address the entanglement between AImodels, the economics of the ongoing fourth industrial revolution, and the socio-ecological. We also explore the implications for mathematics education. Three aspects of mathematical modeling are suggested to be of specific importance for how AI-models pose risks in the socio-ecological: transparency, scale, and the relationship between model and the phenomena that is being modeled. We propose making the examination of mathematical AI-models a central part of future imaginations of a socio-ecological matheracy.

This extended abstract describes work in progress on designing learning materials that introduces data science concepts in mathematics education. The aim of the learning material is specifically to provide conceptual understanding that could facilitate classroom discussions and learning related to ethical and sociopolitical dimensions of societal applications of data science. For the purpose of the symposium, the abstract thus relates to primarily to the topic AI and data science education for social good, but can also be related to learning materials.

Lärarfortbildning genom kollegialt lärande i arbetslaget är vanligt i svenska skolor. Men vad händer om fortbildningen istället sker digitalt i det ”utvidgade kollegiet” via öppna nätbaserade kurser (Massive Open Online Course, förkortat MOOC)? I denna text kommer jag att presentera ett konkret exempel på en MOOC med temat partikelacceleratorer och sedan reflektera över potentialen i sådana kurser för kollegialt lärande kring undervisning inom STEM-ämnen. Utifrån vad forskning säger om lärarfortbildning reflekterar jag över för- och nackdelar jämfört med traditionellt kollegialt lärande, och hur MOOC:ar kan komplettera det traditionella upplägget.

This presentation reports on tentative research results indicating that mathematics teachers need specific kinds of societal awareness to facilitate a classroom discussion on ethical and critical perspectives related the mathematics in the 4th industrial revolution (Big Data, AI, Machine learning, etc.). This awareness relates to societal phenomena that may intersect with mathematical modelling, e.g. data may portray whiteness/sexism, risking mathematical analysis to reproduce them. To facilitate classroom discussions on the intersection, teachers must first be aware of how such phenomena operate in society. Teaching units in Sweden and USA are presented with examples of how mathematics teachers enable discussions through their societal awareness. An implication is that teacher education may require more societal content.

AI and machine learning are increasingly being applied to solve complex societal issues. One approach is to train algorithms on training data that describes human behavior. This is not without potential problems though. For example, historical human behavior may contain biases, and then algorithms may reproduce social injustices. This workshop contained lesson elements from a study where pre-service mathematics teachers conducted lessons to first conceptually teach how mathematical modelling based on machine learning works, and then to broaden the scope to consider critical thinking in relation to ethics and democratic values. The participants were invited to test and discuss the lesson elements.

Implementations of Big Data analysis are reshaping society. The novel ways mathematics operate in society warrants new efforts for mathematics education, both in teaching the new technology and in providing an ethical and critical awareness of its implications. This interview study investigates pre-service teachers' ethical reasoning in data science contexts, focusing on aspects of access to the data that underpin the technology. Findings show that pre-service teachers offer a wide array of ethical arguments related to access to data, that informs their effort to think critically on oppressive situations. However, there is also an indication that their reasoning can be limited by lacking understanding of the related data science methodology, implying that mathematics teacher education should encompass more of this.

The prevalence of Big Data Analytics as a proxy for human decision-makingprocesses in globalized society, has catalyzed a call for the modernization ofthe mathematics curriculum to promote data literacy and ethical reasoning. Tosupport this initiative, ten preservice mathematics teachers (PSTs) in Sweden(SWE) and the United States (US) were interviewed to identify what ethicalconsiderations preservice teachers (PSTs) make in their mathematicalanalyses of data science contexts. Preliminary results indicate that teachersmake a myriad of ethical considerations in their mathematical work that aretied to their critical mathematics consciousness (CMC), conceptions of dataliteracy, and experiences. As a result, it is imperative that educatorssimultaneously design educational curricula to foster students’ CMC and workto transform teacher held definitions of data literacy to reflect changes broughton by globalization.