31. LEd – Network on Language and Education
Paper
Exploring the Application of Artificial Intelligence in Foreign Language Education within School Settings: A Systematic Literature Review
Tamar Mikeladze1, Paulien Meijer2
1Iakob Gogebashvili Telavi State University; 2Radboud University
Presenting Author: Mikeladze, Tamar
Artificial Intelligence in a foreign language education (AIFLED) has been gaining special attention globally. The emergence of Intelligent Tutoring Systems, AI conversational agents, ChatGPT, robots and other AI tools in foreign language learning has prompted a surge in research and recommendations. The previous systematic literature reviews include a focus on the integration and impact of advanced technologies, with an emphasis on Artificial Intelligence in Language Education and a broader examination of new technologies (Liang et al., 2021; Zhang & Zou 2020; Tobing et al. 2023). Pedagogical applications, such as mobile learning, multimedia tools, and digital game elements, are highlighted as effective tools for enhancing language learning experiences and motivation. Furthermore, cognitive aspects in language education has received specific attention. Positive outcomes, including improved language skills and increased learner motivation, have been consistently reported. The studies also recognize challenges and limitations related to technology integration, emphasizing the need for ongoing research to address issues like short intervention periods and the effectiveness of emerging technologies.
The swift technological progress in the field of AI raises a multitude of inquiries and challenges related to utilization of AI in schools, encompassing its impact on language acquisition, affective or psychological states, or assessment methods. There have been several systematic literature reviews on AI in foreign language teaching, human-computer collaboration in language education, and technology-enhanced language learning; however, the research objects are mainly university or college levels and higher education learners (Ji et al., 2022; Zhang & Zou, 2020; Liang et al., 2021; Sharadgah & Sadi, 2022; Tobing et al., 2023). This study aims to add to the current research by focusing on studies in schools settings (K-12).
The objective of this systematic literature review is to investigate and synthesize the applications of AIFLED within school settings. The review aims to provide an understanding of the current state of research and identify emerging trends and gaps in the literature during the period between 2019 and 2023.
The following research questions guided our study:
- What AI tools have been employed in Foreign Language (FL) teaching in schools between 2019 and 2023?
- What pedagogical or foreign language aspects have been researched regarding the AI applications?
- What challenges and opportunities are associated with the integration of AI tools in FL education within school environments?
The conceptual framework for this review is grounded in the intersection of three main pillars:
- Pedagogical Integration: Examining how AI tools are integrated into pedagogical practices in FL teaching. This includes exploring theoretical framework, instructional design and the adaptability of AI tools.
- Learning Outcomes: Evaluating the impact of AI tools on language learning outcomes, including but not limited to linguistic proficiency, cultural understanding, student engagement and perceptions of AI.
- Challenges and Opportunities: Investigating the challenges faced and opportunities presented by the integration of AI tools in FL education. This involves exploring issues such as student acceptance, ethical considerations, and potential enhancements in language learning experiences.
The conceptual framework will guide the systematic analysis of literature, providing a structured approach to understanding application of AI tools in FL teaching in schools from 2019 to 2023.
Methodology, Methods, Research Instruments or Sources UsedThe systematic literature review adhered to PRISMA (2020) guidelines, encompassing three phases: Identification of papers, screening, and inclusion. The criteria for article eligibility included language (English), relevance to foreign language learning, utilization of AI tools, school setting context, empirical data inclusion (qualitative, quantitative, or mixed), publication within the last five years (2019-2023), and publication in scientific papers through peer-reviewed journals.
Exclusion criteria comprised other educational settings like college/university, various types of studies/theoretical descriptions (e.g., descriptive papers, conference papers). Studies related to first language, sign language, or computer language learning were excluded, along with those solely involving teachers and teacher education, as well as studies focused on development, or description of AI tools.
Databases Scopus, Google Scholar, and Web of Science were systematically searched between October and December 2023. Keywords and search strings included terms such as "Foreign language," "Artificial Intelligence," "AI tools," "Machine learning," "Deep learning," "Chatbots," "Speech recognition," "Secondary education," and "Primary/Elementary/Middle/High Schools."
Initially retrieving 16,800 papers on Google Scholar, 13,783 on Web of Science, and 85 on Scopus, the search was refined using keywords and filters, yielding 344 references. These were uploaded to Rayyan.ai and subjected to screening based on titles and abstracts. 280 papers were excluded at this stage; 206 papers were on AI tools at the university/college level, 17 on AI application in translation or linguistics, and 15 offering theoretical reviews of AI tools. Further examination of full texts of 42 papers revealed only 16 empirical studies describing AI tool applications in foreign language classes within a school context.
Data extraction process consisted of specific information extracted from each included study: publication year, school level, study participants, target foreign language, language level, utilized AI tool, procedure, research methods, key findings, and challenges which will be elaborated in our presentation.
Conclusions, Expected Outcomes or FindingsThe research findings across various studies underscore the transformative impact of integrating AI, particularly through the utilization of chatbots and virtual agents, into FL educational settings. A recurring theme across these studies is the substantial improvement in FL learning outcomes. The incorporation of AI has demonstrated notable enhancements in oral English proficiency, vocabulary acquisition, pronunciation, fluency, and language use. Furthermore, AI-supported activities, such as chatbot-assisted dynamic assessment and virtual interactions, have positively influenced speaking competence, listening comprehension, and overall language acquisition.
A significant aspect of AI's role in foreign language education revolves around personalized learning and adaptability. AI tools, particularly chatbots, have been instrumental in providing tailored learning experiences that adapt to individual proficiency levels. The incorporation of adaptive learning paths, facilitated by tailored chatbot features, has been recognized as valuable for refining teaching methods and fostering adaptive learning environments that cater to diverse learner needs.
The studies consistently report positive learner experiences, with participants expressing sustained interest, motivation, and enjoyment when engaging with AI technologies. Additionally, AI chatbots has been associated with a reduction in foreign language anxiety among students. The creation of a supportive and non-critical practice environment by AI has contributed to increased confidence in language use.
However, challenges such as technical issues, the need for human supervision, and potential biases in algorithms are also acknowledged. Common limitations include small-scale designs, variability in experiences, and perceived scenario relevance. Recommendations focus on enhancing realism, addressing technical issues, personalizing learning, providing more feedback, and aligning with national curricula.
Future research should explore individual factors, conduct efficacy studies across proficiency levels, implement user suggestions, consider long-term impacts, incorporate diverse participants, explore proficiency-related preferences, and address cognitive load. Implications emphasize the positive impact of AI chatbots on foreign language learning, but variability in experiences calls for continuous improvement.
ReferencesAthanassopoulos, S., Manoli, P., Gouvi, M., Lavidas, K., & Komis, V. (2023). The use of ChatGPT as a learning tool to improve foreign language writing in a multilingual and multicultural classroom. Advances in Mobile Learning Educational Research, 3, 818–824. https://doi.org/10.25082/AMLER.2023.02.009
Chen Hsieh, J., & Lee, J. S. (2023). Digital storytelling outcomes, emotions, grit, and perceptions among EFL middle school learners: robot-assisted versus PowerPoint-assisted presentations. Computer Assisted Language Learning, 36(5–6), 1088–1115. https://doi.org/10.1080/09588221.2021.1969410
Ericsson, E., Sofkova Hashemi, S., & Lundin, J. (2023). Fun and frustrating: Students’ perspectives on practising speaking English with virtual humans. Cogent Education, 10(1). https://doi.org/10.1080/2331186X.2023.2170088
Han, D.-E. (2020). The Effects of Voice-based AI Chatbots on Korean EFL Middle School Students’ Speaking Competence and Affective Domains. Asia-Pacific Journal of Convergent Research Interchange, 6(7), 71–80. https://doi.org/10.47116/apjcri.2020.07.07
Ji, H., Han, I., & Ko, Y. (2022). A systematic review of conversational AI in language education: focusing on the collaboration with human teachers, Journal of Research on Technology in Education. DOI:10.1080/15391523.2022.2142873
Jeon, J. (2023). Chatbot-assisted dynamic assessment (CA-DA) for L2 vocabulary learning and diagnosis. Computer Assisted Language Learning, 36(7), 1338–1364. https://doi.org/10.1080/09588221.2021.1987272
Lee, S., & Jeon, J. (2022). Visualizing a disembodied agent: young EFL learners’ perceptions of voice-controlled conversational agents as language partners. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2022.2067182
Liang, J., Hwang, G., Chen, M. A., & Darmawansah, D. (2021): Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach, Interactive Learning Environments, DOI: 10.1080/10494820.2021.1958348
Sharadgah, T. A., & Sa’di, R. A. (2022). A systematic review of research on the use of artificial intelli-gence in English language teaching and learning (2015-2021): What are the current effects? Journal of Information Technology Education: Research, 21, 337-377. https://doi.org/10.28945/4999
Tai, T. Y., & Chen, H. H. J. (2020). The impact of Google Assistant on adolescent EFL learners’ willingness to communicate. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1841801
Wang, X., Pang, H., Wallace, M. P., Wang, Q., & Chen, W. (2022). Learners’ perceived AI presences in AI-supported language learning: a study of AI as a humanized agent from community of inquiry. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2022.2056203
Yang, H., Kim, H., Lee, J. H., & Shin, D. (2022). Implementation of an AI chatbot as an English conversation partner in EFL speaking classes. ReCALL, 34(3), 327–343. https://doi.org/10.1017/S0958344022000039
Zhang, R., & Zou, D. (2020). Types, purposes, and effectiveness of state of-the-art technologies for second and foreign language learning. Computer Assisted Language Learning, DOI: 10.1080/09588221.2020.1744666
31. LEd – Network on Language and Education
Paper
Social Media, Linguistic diversity and Language Learning: Bridging Activity at University Level
Liudmila Shafirova1, Boris Vazquez-Calvo2, Maria Helena Araújo e Sá1
1University of Aveiro, Portugal; 2University of Malaga, Spain
Presenting Author: Shafirova, Liudmila
In line with the conference theme “Education in an Age of Uncertainty,” our study explores the concept of “rewilding language education” as proposed by Thorne et al. (2021). This concept emphasizes integrating students’ digital and offline language experiences into classroom learning environments. Our research specifically focuses on the utilization of social media and streaming platforms in fostering autonomous language learning and valorisation of linguistic diversity. This aligns with the Council of Europe's agenda, which focuses on viewing learners as social agents promoting their learning autonomy and engagement (Council of Europe, 2018).
In our study, we introduce a bridging activity in Russian and Spanish language courses at a Portuguese university, tailored for future educators, translators, and linguists. Briging activity is a pedagogical design that aims to seamlessly integrate students’ extracurricular and academic language experiences, enhancing their informal language use (Thorne & Reinhardt, 2008). Previous research on bridging activities with a similar focus on social media showed positive results including target culture awareness development (Miller et al., 2019; Yeh & Mitric, 2021) and socio-pragmatic awareness development (Reinhardt & Ryu, 2013). Most of these studies are focused on text-based social interaction, so there is a gap in research regarding video consumption. Our bridging activity focuses on video consumption, exploring informal language use on social media and streaming platforms, and autonomous learning development.
The bridging activity aims to achieve several objectives:
1. Enhancing language and cultural awareness: By involving students in classroom discussions on language learning through videos and maintaining auto-ethnographic diaries of their video consumption in various languages, the activity encourages the exploration of plurilingual digital landscapes. This approach is supported by studies indicating the effectiveness of similar activities in fostering cultural and socio-pragmatic awareness (Miller et al., 2019; Yeh & Mitric, 2021; Reinhardt & Ryu, 2013).
2. Promoting self-directed learning: The activity is structured to bolster self-directed learning by valuing sharing their learning experiences with their specific strategies and techniques for language learning and foreign language video consumption. This aims to empower students to actively integrate their digital media experiences into their language learning journey.
3. Developing algorithmic and critical awareness: A crucial aspect of our study is to develop students’ critical awareness regarding the dominance of the English language in digital media and the influence of algorithms on content exposure (Jones, 2021). This objective addresses the gap in existing research concerning video-based social interactions and their impact on language learning.
The pedagogical objectives of this bridging activity go hand and hand with our research questions including: 1. What are the benefits and pitfalls of the implemented bridging activity? 2. What are the students’ perceptions of the development of language, cultural and algorithmic awarenesses? 3. What are the students' perceptions on the promotion of self-directed learning?
Overall, our study advocates for a plurilingual stance, valuing learners’ agency and cultural awareness in language education (Marshall & Moore, 2016). This perspective is integral to fostering a more inclusive and diverse linguistic environment in the classroom. The following methods allow us to collect relevant data.
Methodology, Methods, Research Instruments or Sources UsedThis study employs a design-based methodology (McKenney & Reeves, 2014), focusing on theoretical knowledge inquiry and practical application through a specifically tailored bridging activity Multilingualism and diversity in new media. It was implemented in four university language courses (3 Russian, 1 Spanish) with 26 participants in the Spring semester of 2023 at a level of a Bachelor degree of Language and Cultures faculty. The courses were from different levels: 1-Beginner (Russian); 2-Intermediate (Russian) and 3-Advanced (Russian and Spanish). The activity was extra-curricular and was implemented by the first author of the study. The activity comprised three parts:
1) Introducing the project in the classroom, discussing digital landscapes, and reflecting on multilingual video content (2-hour classroom);
2) An auto-ethnographic homework assignment where students documented and analyzed their online video consumption;
3) A follow-up classroom session for discussing the insights gained and creating visual maps reflecting their learning (2-hour classroom).
The classes were given in the target language of the participants mixed with Portuguese and English for comprehension purposes. Objectives of the pedagogical activity were aligned with the research ones including enhancing language awareness, promoting self-directed learning and developing algorithmic and critical awareness. Data were collected from various sources, including 9 autoethnographic diaries, 10 visual maps, teacher observations (950 words), and 26 student questionnaires. Qualitative content analysis was applied to the diaries, maps, and teacher observations (Schreier, 2012), while the questionnaires were analyzed using descriptive statistics. Qualitative analysis categories were constructed by the first author of the study and validated by the co-authors.
Conclusions, Expected Outcomes or FindingsThe preliminary results indicate that according to the questionnaires the students perceived the proposed tasks as successful in developing strategies of autonomous language learning and also in enhancing their learning awareness by noticing out-of-the-classroom language improvements. Also, similar to the previous studies which were successful in developing the target culture awareness (Miller et al., 2019; Yeh & Mitric, 2021), in this bridging activity, the students mentioned that social media helped them to become aware of getting to know different linguistic varieties of their target languages, noticing cultural differences and peculiarities, and being more open to learning new languages.
The teacher observations and students’ auto-ethnographic diaries indicated that almost all of the students used English in their social media before the activity, with very limited exposure to other languages. Due to questionnaires and visual maps, after the activity students tended to value the development of algorithmic awareness, and to diversify language exposure on social media (Jones, 2021).
Interestingly, due to the teacher’s observations, beginner languages courses showed more enthusiasm in integrating these strategies into their learning, suggesting a potential area for further research. This contrasts with typical beginner language courses that focus on comprehensible input (Patrick, 2019). In summary, this study provides insights for curriculum development in higher education language courses suggesting transferable and transdisciplinary tasks for autonomous learning development. As a practical output, we will present a handbook of comprehensive materials from the bridging activity for language educators, contributing to knowledge transfer within the community of language educators.
ReferencesCouncil of Europe. (2018). Common European framework of reference for languages: Learning, teaching, assessment. Companion volume with new descriptors. https://rm. Coe. int/cefr-companion-volume-with-new-descriptors-2018/1680787989
Jones, R. (2021). The text is reading you: Teaching language in the age of the algorithm. Linguistics and Education (62), 1-7. https://doi.org/10.1016/j.linged.2019.100750
Marshall, S., & Moore, D. (2016). Plurilingualism amid the panoply of lingualisms: Addressing critiques and misconceptions in education. International Journal of Multilingualism, 15(1), 19−34, https://doi.org/10.1080/14790718.2016.1253699
McKenney, S. E., & Reeves, T. C. (2013). Educational design research. In J. M. Spector, M. D. Merrill, J. Elan, & M. J. Bishop (Eds.), The handbook of research on educational and communications technology (131-140). Springer.
Miller, A. M., Morgan, W. J., & Koronkiewicz, B. (2019). Like or tweet: Analysis of the use of Facebook and Twitter in the language classroom. TechTrends, 63, 550−558.
Patrick, R. (2019). Comprehensible Input and Krashen's theory. Journal of Classics Teaching, 20(39), 37-44.
Reinhardt, J., & Ryu, J. (2013). Using social network-mediated bridging activities to develop socio-pragmatic awareness in elementary Korean. International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), 3(3), 18−33. https://doi.org/10.4018/ijcallt.2013070102
Schreier, M. (2012). Qualitative content analysis in practice. Sage Publications Ltd.
Thorne, S. L., Hellermann, J., and Jakonen, T. (2021). Rewilding language education: Emergent assemblages and entangled actions. The Modern Language Journal, 105(1), 106-125.
Thorne, S. L., & Reinhardt, J. (2008). “Bridging activities,” new media literacies, and advanced foreign language proficiency. CALICO Journal, 25, 558–572. https://doi.org/10.1558/cj.v25i3.558-572
Yeh, E., & Mitric, S. (2021). Social media and learners-as-ethnographers approach: increasing target-language participation through community engagement. Computer Assisted Language Learning, 1–29. https://doi.org/10.1080/09588221.2021.2005630
Zhang, L. T., and Vazquez-Calvo, B. (2022). “¿Triste estás? I don’t know nan molla” Multilingual pop song fandubs by@ miree_music. ITL-International Journal of Applied Linguistics, 173(2), 197-227.
31. LEd – Network on Language and Education
Paper
(Re-)Production of Linguicism through AI-based NLP Technology in Higher Education in Austria and Germany
Marion Döll, Sabine Guldenschuh, Tanja Tajmel
Universität Flensburg, Germany
Presenting Author: Döll, Marion;
Guldenschuh, Sabine
ducation is of central relevance for social and system integration in multilingual European migration societies such as Austria and Germany (Hadjar & Becker 2019). In Austria and Germany, educational inequality can be observed for immigrant students at all stages of education, in correlation with disadvantaged socio-economic status and multilingualism (Dobutowitsch 2020, Döll & Knappik 2015, Ebert & Heublein 2017, Hinz & Thielemann 2013, OECD 2023, Unger et al. 2019).
It seems logical to pick up on recent developments in the field of artificial intelligence (AI) and discuss the potential of assistive AI technology such as natural language processing (NLP) tools for reducing language-based discrimination. AI-based NLP tools have already found their way into educational institutions worldwide: They are used for assessment and evaluation (e.g. feedback), management of learning processes (e.g. learning analytics), as assistants (e.g. for making contact), in the form of intelligent tutor systems and for the design of quasi-authentic meaning-focused tasks (Crompton & Burke 2023), and students use NLP tools to search for articles, translate, structure and edit texts (Garrel & Mayer 2023). It is widely recognized that AI has the potential to increase educational equality, but also carries the risk of making equal participation more difficult (GI 2023). At present, discrimination through AI is mainly discussed in terms of disadvantages due to various forms of algorithmic bias (Baker & Hawn 2021). From a power-critical anti-racist perspective, the question arises as to what extent the institutional regulation of access to assistive AI-supported NLP tools (re)produces inclusion and exclusion in education: Who is allowed to use AI-assisted NLP tools and in which situations? How are restrictions argued?
In recent years, in official German-speaking countries the term linguicism became established to describe language-related discrimination in the context of migration and multilingualism (Skutnabb-Kangas 2015). The term describes "ideologies, structures and practices which are used to legitimate, effectuate, regulate and reproduce an unequal division of power and resources (both material and immaterial) between groups which are defined on the basis of language" (Skutnabb-Kangas 1988: p. 13). Linguicism is therefore more likely to be understood as structural discrimination, which can have effects on the macro, meso and micro levels of education systems. According to Skutnabb-Kangas (2015), if the education policy of a multilingual migration society prioritizes a monolingual education system, this is linguicism at the macro level. At the meso and micro level, linguicism can occur in various forms of direct and indirect institutional discrimination (Dovidio et al. 2010, Gomolla 2023), e.g. by banning specific languages on campus or when lecturers also take linguistic aspects such as accents, sociolects or the fact of a multilingual biography into account when assessing academic performance (Döll & Knappik 2015, Dobutowitsch 2020).
Following the understanding of linguicism as a social structure, it has to be assumed that students will be allowed to use AI-supported generative NLP technology to improve the production and reception of texts to varying degrees depending on their and their family’s migration and language biography. For multilingual students from immigrant families, the strongest restrictions tend to be expected, especially in nation-state contexts such as Germany and Austria, which are characterized by neo-assimilationism (Nieke 2006, Döll 2019).
At a time when universities around the world are discussing how to deal with AI, we will use the example of two universities from Austria and Germany to examine the extent to which linguicist tendencies are emerging in the discourses on AI-supported generative NLP technology at the meso and micro level of higher education.
Methodology, Methods, Research Instruments or Sources UsedIn order to reconstruct the processes of (re)production of linguicism in connection with AI-based generative NLP technology, in an exploratory and open-ended qualitative research project based on grounded theory (Charmaz 2006) data has been continuously collected on an occasional basis since spring 2023. The open multi-method approach makes it possible to capture the dynamic discourses and developments on the topic.
So far, participant observations have been carried out in five courses for lecturers at the two universities with a focus on the thematization of language-related discrimination. The field notes taken were first analyzed in terms of content and then specific situations were examined using key incident analysis, which reveals practices of a social group without applying a complete ethnography (Erickson 1986). In addition, the policy papers and information on AI-based generative NLP technology in university teaching for university lecturers and students are analyzed using critical discourse analysis (CDA, Wodak & Meyer 2016).
In order to be able to describe the lecturers' ways of approaching AI-based generative NLP tools, including the implementation of the universities' guidelines, in their courses and examinations with descriptive statistics, a quantitative survey by means of an online questionnaire for students is prepared for spring term 2024. If beneficial to our research project, in-depth interviews or group discussions will be conducted in the autumn term to clarify the statistical results.
Conclusions, Expected Outcomes or FindingsAt the moment, we assume that we will be able to present the results of the CDA of the guidelines and the key incident analysis as well as the initial results of the quantitative survey. In line with the mechanisms of structural discrimination in democratic states, we assume that there won’t be linguicist inequality between monolingual and multilingual or native and immigrant students in connection with AI-based generative NLP tools in the meso-level guidelines, as this would contradict the democratic principle of equal treatment. However, the interim results of the analyses of the field notes from the participant observations indicate a limited awareness of the potential for discrimination of AI-based generative NLP tools among both university lecturers and further education lecturers, so that we assume that linguicist speech and actions are experienced at the micro level, i.e. in the interaction between students and lecturers. Due to the similar migration histories and migration discourses in Austria and Germany, we do not expect any national differences at present, but this assumption still needs to be checked with the data.
In any case, our work, which is located at the intersection of educational science, linguistics and the sociology of technology, offers initial findings on the question of whether linguicist routines are becoming established in higher education institutions in connection with AI-based generative NLP tools and raises new research questions in this field.
ReferencesBaker, R. S., & Hawn, A. (2021). Algorithmic Bias in Education. International Journal of Artificial Intelligence in Education 32, 1052-1092.
Charmaz, K. (2006). Constructing grounded theory. London.
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. Int J Educ Technol High Educ 20, 22.
Dobutowitsch, F. (2020). Lebensweltliche Mehrsprachigkeit an der Hochschule. Münster.
Döll, M., & Knappik, M. (2015) Institutional mechanisms of inclusion and exclusion in Austrian pre-service teacher education. Tertium comparationis 21 (2015) 2, 185-204.
Döll, M. (2019). Sprachassimilativer Habitus in Bildungsforschung, Bildungspolitik und Bildungspraxis. ÖDaF, 1+2/2019, 191-206.
Dovidio, J. F. et al. (2010). Prejudice, stereotyping and discrimination: Theoretical and empirical overview. In J. F. Dovidio et al. (Ed.), The Sage handbook of prejudce, streotyping and discrimination (pp 3–28). Los Angeles.
Ebert, J., & Heublein, U. (2017). Studienabbruch bei Studierenden mit Migrationshintergrund. Hannover.
Erickson, F. (1986). Qualitative methods in research on teaching. In M. C. Wittrock (Ed.), Handbook of research on teaching (pp 119—161). New York.
Garrel, J., & Mayer, J. (2023). Artificial Intelligence in studies—use of ChatGPT and AI-based tools among students in Germany. Humanities and Social Sciences Communications, 10(1), 1-9.
Gesellschaft für Informatik (GI) (2023). Künstliche Intelligenz in der Bildung. Positionspapier. https://gi.de/fileadmin/GI/Hauptseite/Service/Publikationen/GI_Positionspapier_KI_in_der_Bildung_2023-07-12.pdf
Gomolla, M. (2023). Direkte und indirekte, institutionelle und strukturelle Diskriminierung. In A. Scherr et al. (Ed.), Handbuch Diskriminierung (2nd edn, pp 171-194). Wiesbaden.
Hinz, T., & Thielemann, T. (2013). Studieren mit Migrationshintergrund an einer deutschen Universität. Soziale Welt, 64(4), 381–399.
OECD (2023). PISA 2022 Results (Volume I): The State of Learning and Equity in Education, PISA. Paris.
Nieke, W. (2006). Anerkennung von Diversität als Alternative zwischen Multikulturalismus und Neo-Assimilationismus? In H.-U. Otto & M. Schrödter (Ed.), Soziale Arbeit in der Migrationsgesellschaft (pp 40-48). Lahnstein.
Skutnabb-Kangas, T. (1988). Multilingualism and the Education of Minority Children. In T. Skutnabb-Kangas & J. Cummins (Ed.), Minority education: from shame to struggle (pp 7-44). Clevedon.
Skutnabb-Kangas, T. (2015). Linguicism. The Encyclopedia of Applied Linguistics. Malden, MA.
Unger, M. et al. (2019). Studierenden-Sozialerhebung 2019. Wien.
Wodak, R., & Meyer, M. (Ed.) (2016). Methods of critical discourse studies (3rd edn). London.
|
