Showing 1–14 of 14 results for author: Viberg, O

Teachers' Perceived Benefits and Risks of AI Across Fifty-Five Countries: An Audit of LLM Alignment and Steerability

Authors: Yan Tao, Olga Viberg, Deepak Varuvel Dennison, Zhikun Wu, René F. Kizilcec

Abstract: Teachers' trust in artificial intelligence (AI) in education depends on how they balance its perceived benefits and risks. Yet global discussions about scaling AI in education rely on fragmented evidence, as most studies of teachers' perceptions focus on single countries or small samples. This lack of representative cross-national evidence limits both theory building and policy development. At the… ▽ More Teachers' trust in artificial intelligence (AI) in education depends on how they balance its perceived benefits and risks. Yet global discussions about scaling AI in education rely on fragmented evidence, as most studies of teachers' perceptions focus on single countries or small samples. This lack of representative cross-national evidence limits both theory building and policy development. At the same time, large language models (LLMs) are increasingly used in research, policy, and teachers' professional workflows, despite limited validation in education. To address these gaps, we conduct a large-scale audit of LLM alignment with teachers' perceptions of AI by combining representative international survey data with systematic model evaluation. Using OECD TALIS data from 55 countries and territories, we measure cross-national variation in teachers' perceived benefits and risks of AI. We then benchmark responses from eight state-of-the-art LLMs across four providers under both general and country-specific prompting, comparing higher- and lower-reasoning models. Results reveal substantial cross-national variation in teacher perceptions that is not reliably reflected in LLM outputs. Models compress country differences, overestimate both benefits and risks, and show limited gains from identity prompting or enhanced reasoning. This misalignment matters because LLM-generated guidance and professional discourse increasingly shape how teachers learn about and discuss AI, potentially influencing trust and future adoption decisions. Our findings caution against treating LLM outputs as substitutes for direct engagement with teachers when informing global AI-in-education initiatives. At the same time, some models (e.g., Gemini 3 Fast) partially capture cross-national ranking patterns, suggesting a complementary role in hypothesis generation and exploratory comparative analysis. △ Less

Submitted 8 May, 2026; originally announced May 2026.

Comments: Accepted as full paper to the 13th ACM Conference on Learning @ Scale (L@S'26)

ProPACT: A Proactive AI-Driven Adaptive Collaborative Tutor for Pair Programming

Authors: Anahita Golrang, Kshitij Sharma, olga viberg

Abstract: Effective pair programming depends on coordination of attention, cognitive effort, and joint regulation over time, yet most adaptive learning systems remain individual-centric and reactive. This paper introduces ProPACT, a proactive AI-driven adaptive collaborative tutor that treats collaboration itself as the object of instruction. ProPACT constructs a multimodal dyadic learner model based on Joi… ▽ More Effective pair programming depends on coordination of attention, cognitive effort, and joint regulation over time, yet most adaptive learning systems remain individual-centric and reactive. This paper introduces ProPACT, a proactive AI-driven adaptive collaborative tutor that treats collaboration itself as the object of instruction. ProPACT constructs a multimodal dyadic learner model based on Joint Visual Attention (JVA), Joint Mental Effort (JME), and individual mental effort, and employs an XGBoost-based forecasting model to predict emerging suboptimal collaboration states up to 30 seconds in advance. These predictions drive a hierarchical adaptive policy that delivers minimally intrusive scaffolds while fading support during productive collaboration. A within-subject study with 26 pair-programming dyads shows that proactive feedback significantly improves debugging success, task efficiency, feedback uptake, and post-intervention gains in JVA and JME, demonstrating the potential of forecast-driven dyadic adaptivity for real-time collaborative learning regulation. △ Less

Submitted 4 May, 2026; originally announced May 2026.

From Intention to Text: AI-Supported Goal Setting in Academic Writing

Authors: Yueling Fan, Richard Lee Davis, Olga Viberg

Abstract: This study presents WriteFlow, an AI voice-based writing assistant designed to support reflective academic writing through goal-oriented interaction. Academic writing involves iterative reflection and evolving goal regulation, yet prior research and a formative study with 17 participants show that writers often struggle to articulate and manage changing goals. While commonly used AI writing tools… ▽ More This study presents WriteFlow, an AI voice-based writing assistant designed to support reflective academic writing through goal-oriented interaction. Academic writing involves iterative reflection and evolving goal regulation, yet prior research and a formative study with 17 participants show that writers often struggle to articulate and manage changing goals. While commonly used AI writing tools emphasize efficiency, they offer limited support for metacognition and writer agency. WriteFlow frames AI interaction as a dialogic space for ongoing goal articulation, monitoring, and negotiation grounded in writers' intentions. Findings from a Wizard-of-Oz study with 12 expert users show that WriteFlow scaffolds metacognitive regulation and reflection-in-action by supporting iterative goal refinement, maintaining goal-text alignment during drafting, and prompting evaluation of goal fulfillment. We discuss design implications for AI writing systems that prioritize reflective dialogue, flexible goal structures, and multi-perspective feedback to support intentional and agentic writing. △ Less

Submitted 17 April, 2026; originally announced April 2026.

Comments: Accepted at AIED 2026

Who Decides in AI-Mediated Learning? The Agency Allocation Framework

Authors: Conrad Borchers, Olga Viberg, René F. Kizilcec

Abstract: As AI-mediated learning systems increasingly shape how learners plan, make decisions, and progress through education, learner agency is becoming both more consequential and harder to conceptualize at scale. Existing research often treats agency as a proxy for engagement and self-regulation, leaving unclear who actually holds decision-making authority in large-scale, automated learning environments… ▽ More As AI-mediated learning systems increasingly shape how learners plan, make decisions, and progress through education, learner agency is becoming both more consequential and harder to conceptualize at scale. Existing research often treats agency as a proxy for engagement and self-regulation, leaving unclear who actually holds decision-making authority in large-scale, automated learning environments. This paper reframes learner agency as the allocation of decision authority across learners, educators, institutions, and AI systems. We introduce the Agency Allocation Framework (AAF) for analyzing how decisions are distributed, how choices are architected, what evidence supports them, and over what time horizons their consequences unfold. Drawing on a focused review of Learning at Scale literature and an illustrative tutoring-system example, we identify four recurring challenges for studying learner agency at scale: (1) conceptual ambiguity, (2) reliance on behavioral proxies, (3) trade-offs between efficiency and learner control, and (4) the redistribution of agency through AI-mediated systems. Rather than advocating more or less automation, the AAF supports systematic analysis of when AI scaffolds learners' capacity to act and when it substitutes for it. By making decision authority explicit, the framework provides researchers and designers with analytic tools for studying, comparing, and evaluating agency-preserving learning systems in increasingly automated educational contexts. △ Less

Submitted 9 May, 2026; v1 submitted 15 April, 2026; originally announced April 2026.

Comments: Accepted as full paper to the 13th ACM Conference on Learning @ Scale (L@S '26)

The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?

Authors: Jennifer Meyer, Olaf Köller, Thorben Jansen, Johanna Fleckenstein, Michael W. Asher, Sarah Bichler, Laura Brandl, Jasmin Breitwieser, Kai S. Cortina, Mutlu Cukurova, Martin Daumiller, Hannah Deininger, Frank Fischer, Dragan Gašević, Jeanine Grütter, Anna Hilz, Ioana Jivet, Jelena Jovanović, Rene F. Kizilcec, Livia Kuklick, Marlit Annalena Lindner, Anastasiya Lipnevich, Ute Mertens, Detmar Meurers, Kou Murayama , et al. (11 additional authors not shown)

Abstract: With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences o… ▽ More With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences on the use of generative AI for feedback and its promises and risks in educational practice. We highlight points of convergence in the scholarship, identify areas of debate and unresolved challenges, and outline open questions and future directions for research and educational practice that emerged from structured small-group activities designed to bridge disciplinary barriers. △ Less

Submitted 12 March, 2026; originally announced March 2026.

Protecting and Promoting Human Agency in Education in the Age of Artificial Intelligence

Authors: Olga Viberg, Mutlu Cukurova, Rene F. Kizilcec, Simon Buckingham Shum, Dorottya Demszky, Dragan Gašević, Thorben Jansen, Ioana Jivet, Jelena Jovanovic, Jennifer Meyer, Kou Murayama, Zach Pardos, Chris Piech, Nikol Rummel, Naomi E. Winstone

Abstract: Human agency is crucial in education and increasingly challenged by the use of generative AI. This meeting report synthesizes interdisciplinary insights and conceptualizes four aspects that delineate human agency: human oversight, AI-human complementarity, AI competencies, and relational emergence. We explore practical dilemmas for protecting and promoting agency, focusing on normative constraints… ▽ More Human agency is crucial in education and increasingly challenged by the use of generative AI. This meeting report synthesizes interdisciplinary insights and conceptualizes four aspects that delineate human agency: human oversight, AI-human complementarity, AI competencies, and relational emergence. We explore practical dilemmas for protecting and promoting agency, focusing on normative constraints, transparency, and cognitive offloading, and highlight key tensions and implications to inform ethical and effective AI integration in education. △ Less

Submitted 23 February, 2026; originally announced February 2026.

Comments: O.V., M.C., and R.F.K. organized the meeting and wrote the first version of the report. All authors contributed to the revision of the manuscript, and read and approved the final version

Cultural Differences in Students' Privacy Concerns in Learning Analytics across Germany, South Korea, Spain, Sweden, and the United States

Authors: Olga Viberg, René F. Kizilcec, Ioana Jivet, Alejandra Martínez Monés, Alice Oh, Chantal Mutimukwe, Stefan Hrastinski, Maren Scheffel

Abstract: Applications of learning analytics (LA) can raise concerns from students about their privacy in higher education contexts. Developing effective privacy-enhancing practices requires a systematic understanding of students' privacy concerns and how they vary across national and cultural dimensions. We conducted a survey study with established instruments to measure privacy concerns and cultural value… ▽ More Applications of learning analytics (LA) can raise concerns from students about their privacy in higher education contexts. Developing effective privacy-enhancing practices requires a systematic understanding of students' privacy concerns and how they vary across national and cultural dimensions. We conducted a survey study with established instruments to measure privacy concerns and cultural values for university students in five countries (Germany, South Korea, Spain, Sweden, and the United States; N = 762). The results show that students generally trusted institutions with their data and disclosed information as they perceived the risks to be manageable even though they felt somewhat limited in their ability to control their privacy. Across the five countries, German and Swedish students stood out as the most trusting and least concerned, especially compared to US students who reported greater perceived risk and less control. Students in South Korea and Spain responded similarly on all five privacy dimensions (perceived privacy risk, perceived privacy control, privacy concerns, trusting beliefs, and non-self-disclosure behavior), despite their significant cultural differences. Culture measured at the individual level affected the antecedents and outcomes of privacy concerns more than country-level culture. Perceived privacy risk and privacy control increase with power distance. Trusting beliefs increase with a desire for uncertainty avoidance and lower masculinity. Non-self-disclosure behaviors rise with power distance and masculinity, and decrease with more uncertainty avoidance. Thus, cultural values related to trust in institutions, social equality and risk-taking should be considered when developing privacy-enhancing practices and policies in higher education. △ Less

Submitted 11 February, 2024; v1 submitted 4 December, 2023; originally announced December 2023.

What Explains Teachers' Trust of AI in Education across Six Countries?

Authors: Olga Viberg, Mutlu Cukurova, Yael Feldman-Maggor, Giora Alexandron, Shizuka Shirai, Susumu Kanemune, Barbara Wasson, Cathrine Tømte, Daniel Spikol, Marcelo Milrad, Raquel Coelho, René F. Kizilcec

Abstract: With growing expectations to use AI-based educational technology (AI-EdTech) to improve students' learning outcomes and enrich teaching practice, teachers play a central role in the adoption of AI-EdTech in classrooms. Teachers' willingness to accept vulnerability by integrating technology into their everyday teaching practice, that is, their trust in AI-EdTech, will depend on how much they expect… ▽ More With growing expectations to use AI-based educational technology (AI-EdTech) to improve students' learning outcomes and enrich teaching practice, teachers play a central role in the adoption of AI-EdTech in classrooms. Teachers' willingness to accept vulnerability by integrating technology into their everyday teaching practice, that is, their trust in AI-EdTech, will depend on how much they expect it to benefit them versus how many concerns it raises for them. In this study, we surveyed 508 K-12 teachers across six countries on four continents to understand which teacher characteristics shape teachers' trust in AI-EdTech, and its proposed antecedents, perceived benefits and concerns about AI-EdTech. We examined a comprehensive set of characteristics including demographic and professional characteristics (age, gender, subject, years of experience, etc.), cultural values (Hofstede's cultural dimensions), geographic locations (Brazil, Israel, Japan, Norway, Sweden, USA), and psychological factors (self-efficacy and understanding). Using multiple regression analysis, we found that teachers with higher AI-EdTech self-efficacy and AI understanding perceive more benefits, fewer concerns, and report more trust in AI-EdTech. We also found geographic and cultural differences in teachers' trust in AI-EdTech, but no demographic differences emerged based on their age, gender, or level of education. The findings provide a comprehensive, international account of factors associated with teachers' trust in AI-EdTech. Efforts to raise teachers' understanding of, and trust in AI-EdTech, while considering their cultural values are encouraged to support its adoption in K-12 education. △ Less

Submitted 2 February, 2024; v1 submitted 4 December, 2023; originally announced December 2023.

Kattis vs. ChatGPT: Assessment and Evaluation of Programming Tasks in the Age of Artificial Intelligence

Authors: Nora Dunder, Saga Lundborg, Olga Viberg, Jacqueline Wong

Abstract: AI-powered education technologies can support students and teachers in computer science education. However, with the recent developments in generative AI, and especially the increasingly emerging popularity of ChatGPT, the effectiveness of using large language models for solving programming tasks has been underexplored. The present study examines ChatGPT's ability to generate code solutions at dif… ▽ More AI-powered education technologies can support students and teachers in computer science education. However, with the recent developments in generative AI, and especially the increasingly emerging popularity of ChatGPT, the effectiveness of using large language models for solving programming tasks has been underexplored. The present study examines ChatGPT's ability to generate code solutions at different difficulty levels for introductory programming courses. We conducted an experiment where ChatGPT was tested on 127 randomly selected programming problems provided by Kattis, an automatic software grading tool for computer science programs, often used in higher education. The results showed that ChatGPT independently could solve 19 out of 127 programming tasks generated and assessed by Kattis. Further, ChatGPT was found to be able to generate accurate code solutions for simple problems but encountered difficulties with more complex programming tasks. The results contribute to the ongoing debate on the utility of AI-powered tools in programming education. △ Less

Submitted 2 December, 2023; originally announced December 2023.

Comments: 10 pages, 2 figures, 3 tables. (Pre-print). Final version to be submitted to ACM Journals. LAK2024, March,18-22, 2024, Kyoto, Japan

ACM Class: I.2.0

Cultural Bias and Cultural Alignment of Large Language Models

Authors: Yan Tao, Olga Viberg, Ryan S. Baker, Rene F. Kizilcec

Abstract: Culture fundamentally shapes people's reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people's authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five wid… ▽ More Culture fundamentally shapes people's reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people's authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five widely used large language models (OpenAI's GPT-4o/4-turbo/4/3.5-turbo/3) by comparing the models' responses to nationally representative survey data. All models exhibit cultural values resembling English-speaking and Protestant European countries. We test cultural prompting as a control strategy to increase cultural alignment for each country/territory. For recent models (GPT-4, 4-turbo, 4o), this improves the cultural alignment of the models' output for 71-81% of countries and territories. We suggest using cultural prompting and ongoing evaluation to reduce cultural bias in the output of generative AI. △ Less

Submitted 26 June, 2024; v1 submitted 23 November, 2023; originally announced November 2023.

Journal ref: PNAS Nexus, Volume 3, Issue 9, September 2024, pgae346

Designing Culturally Aware Learning Analytics: A Value Sensitive Perspective

Authors: Olga Viberg, Ioana Jivet, Maren Scheffel

Abstract: This chapter aims to stress the importance of addressing culture when designing and implementing learning analytics services. Learning analytics have been implemented in different countries with the purpose of improving learning and supporting teaching; yet, largely at a limited scale and so far with limited evidence of achieving their purpose. Even though some solutions seem promising, their tran… ▽ More This chapter aims to stress the importance of addressing culture when designing and implementing learning analytics services. Learning analytics have been implemented in different countries with the purpose of improving learning and supporting teaching; yet, largely at a limited scale and so far with limited evidence of achieving their purpose. Even though some solutions seem promising, their transfer from one country to another might prove challenging and sometimes impossible due to various technical, social, contextual and cultural factors. In this chapter, we argue for a need to carefully consider one of these factors, namely cultural values when designing and implementing learning analytics systems. Viewing culture from a value-sensitive perspective, in this chapter, we: 1)exemplify two selected values (i.e. privacy and autonomy) that might play a significant role in the design of learning analytics systems, and 2)discuss opportunities for applying culture-and value-sensitive design methods that can guide the design of culturally aware learning analytics systems. Finally, a set of design implications for culturally aware and value-sensitive learning analytics services is offered. △ Less

Submitted 19 December, 2022; originally announced December 2022.

Comments: 20

Students' Information Privacy Concerns in Learning Analytics: Towards a Model Development

Authors: Chantal Mutimukwe, Jean Damascene Twizeyimana, Olga Viberg

Abstract: The widespread interest in learning analytics (LA) is associated with increased availability of and access to student data where students' actions are monitored, collected, stored and analysed. The availability and analysis of such data is argued to be crucial for improved learning and teaching. Yet, these data can be exposed to misuse, for example to be used for commercial purposes, consequently,… ▽ More The widespread interest in learning analytics (LA) is associated with increased availability of and access to student data where students' actions are monitored, collected, stored and analysed. The availability and analysis of such data is argued to be crucial for improved learning and teaching. Yet, these data can be exposed to misuse, for example to be used for commercial purposes, consequently, resulting in information privacy concerns (IPC) of students who are the key stakeholders and data subjects in the LA context. The main objective of this study is to propose a theoretical model to understand the IPC of students in relation to LA. We explore the IPC as a central construct between its two antecedents: perceived privacy vulnerability and perceived privacy control, and its consequences, trusting beliefs and self-disclosure behavior. Although these relationships have been investigated in other contexts, this study aims to offer mainly theoretical insights on how these relationships may be shaped in the context of LA in higher education. Understanding students' IPC, the related root causes and consequences in LA is the key step to a more comprehensive understanding of privacy issues and the development of effective privacy practices that would protect students' privacy in the evolving setting of data-driven higher education. △ Less

Submitted 31 August, 2021; originally announced September 2021.

Comments: 11 pages

Desperately seeking the impact of learning analytics in education at scale: Marrying data analysis with teaching and learning

Authors: Olga Viberg, Ake Gronlund

Abstract: Learning analytics (LA) is argued to be able to improve learning outcomes, learner support and teaching. However, despite an increasingly expanding amount of student (digital) data accessible from various online education and learning platforms and the growing interest in LA worldwide as well as considerable research efforts already made, there is still little empirical evidence of impact on pract… ▽ More Learning analytics (LA) is argued to be able to improve learning outcomes, learner support and teaching. However, despite an increasingly expanding amount of student (digital) data accessible from various online education and learning platforms and the growing interest in LA worldwide as well as considerable research efforts already made, there is still little empirical evidence of impact on practice that shows the effectiveness of LA in education settings. Based on a selection of theoretical and empirical research, this chapter provides a critical discussion about the possibilities of collecting and using student data as well as barriers and challenges to overcome in providing data-informed support to educators' everyday teaching practices. We argue that in order to increase the impact of data-driven decision-making aimed at students' improved learning in education at scale, we need to better understand educators' needs, their teaching practices and the context in which these practices occur, and how to support them in developing relevant knowledge, strategies and skills to facilitate the data-informed process of digitalization of education. △ Less

Submitted 14 May, 2021; originally announced May 2021.

Fostering learners' self-regulation and collaboration skills and strategies for mobile language learning beyond the classroom

Authors: Olga Viberg, Agnes Kukulska-Hulme

Abstract: Many language learners need to be supported in acquiring a second or foreign language quickly and effectively across learning environments beyond the classroom. The chapter argues that support should focus on the development of two vital learning skills, namely being able to self-regulate and to collaborate effectively in the learning process. We base our argumentation on the theoretical lenses of… ▽ More Many language learners need to be supported in acquiring a second or foreign language quickly and effectively across learning environments beyond the classroom. The chapter argues that support should focus on the development of two vital learning skills, namely being able to self-regulate and to collaborate effectively in the learning process. We base our argumentation on the theoretical lenses of self-regulated learning (SRL) and collaborative learning in the context of mobile situated learning that can take place in a variety of settings. The chapter examines a sample of selected empirical studies within the field of mobile-assisted language learning with a twofold aim. Firstly, the studies are analyzed in order to understand the role of learner self-regulation and collaboration while acquiring a new language beyond the classroom. Secondly, we aim to provide a deeper understanding of any mechanisms provided to develop or support language learners' self-regulated and collaborative learning skills. Finally, we propose that fostering SRL and collaborative learning skills and strategies will benefit from recent advances in the fields of learning analytics and artificial intelligence, coupled with the use of mobile technologies and self-monitoring mechanisms. The ultimate aim is to enable the provision of individual adaptive learning paths to facilitate language learning beyond the classroom. △ Less

Submitted 20 March, 2021; originally announced April 2021.

Comments: In H. Reinders, C. Lai, & P. Sundqvist (Eds.) Routledge Handbook of Language Teaching and Learning beyond the classroom. Routledge. (in press)