Open topics

Food systems are a major contributor to global environmental degradation, with dietary choices accounting for roughly 25% of greenhouse gas emissions (Crippa et al., 2021). Despite growing awareness, consumers still struggle to assess the sustainability of food products at the point of sale (Camilleri et al., 2019). This is partly due to the complexity of interpreting sustainability labels and partly due to behavioural barriers. Even when information is available, the intention-behaviour gap often persists (Carrington et al., 2014).

The rise of online grocery shopping allows consumers to make food choices from home (GroceryDoppio, 2023) and opens new opportunities to support more sustainable decisions through targeted interface design (De Bauw et al., 2022). However, real-time sustainability feedback, a potentially powerful intervention, remains underexplored in digital food environments (Shin et al., 2020).

This thesis aims to conceptualize an intervention that combines product-level sustainability information (Clark et al., 2022; Poore & Nemecek, 2018; Ritchie et al., 2022) with real-time, basket-level feedback in an online grocery setting. For example, shoppers could be shown a composite score summarising the overall environmental impact of their basket, based on criteria such as greenhouse gas emissions, land use, or animal welfare. An online experiment should be conducted to test the influence of this feedback on consumer decision-making. If needed, the intervention can be implemented as an interactive prototype or mock-up.

The experiment will be implemented in collaboration with researchers from the Nürnberg Institute for Market Decisions (NIM), who will co-supervise the thesis.

Prerequisites:

• Required:
  • Enrolled Master Student in International Information Systems, International Business Studies, Economics, Marketing, etc. Bachelor students can only be considered upon careful consideration of timeline feasibility.
  • Interest in advancing research in the food domain.
  • Willingness to write their thesis in English.
• Highly Desirable:
  • Previous experience in experimental methodology, e.g., through our courses “Experimentelle Verhaltensforschung in Data Science (EVIDS)” (Bachelor) or the seminar “Information Systems for Behavior Change (ISBC)” (Master).
  • Previous knowledge and/or experience in the application of statistical methods (e.g., ANOVA, LMM, etc.)

References

• Camilleri, A. R., Larrick, R. P., Hossain, S., & Patino-Echeverri, D. (2019). Consumers underestimate the emissions associated with food but are aided by labels. Nature Climate Change, 9(1), 53-58.
• Carrington, M. J., Neville, B. A., & Whitwell, G. J. (2014). Lost in translation: Exploring the ethical consumer intention–behavior gap. Journal of business research, 67(1), 2759-2767.
• Clark, M., Springmann, M., Rayner, M., Scarborough, P., Hill, J., Tilman, D., … & Harrington, R. A. (2022). Estimating the environmental impacts of 57,000 food products. Proceedings of the National Academy of Sciences, 119(33), e2120584119.
• Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F. N., & Leip, A. J. N. F. (2021). Food systems are responsible for a third of global anthropogenic GHG emissions. Nature food, 2(3), 198-209.
• De Bauw, M., De La Revilla, L. S., Poppe, V., Matthys, C., & Vranken, L. (2022). Digital nudges to stimulate healthy and pro-environmental food choices in E-groceries. Appetite, 172, 105971.
• GroceryDoppio. (2023, February 7). January 2023: State of Digital Grocery Performance Scorecard. Performance Scorecard. https://www.grocerydoppio.com/performance-scorecard/january-2023-state-of-digital-grocery-performance-scorecard
• Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987-992.
• Ritchie, H., Rosado, P., & Roser, M. (2022). Environmental Impacts of Food Production. Environmental Impacts of Food Production. https://ourworldindata.org/environmental-impacts-of-food
• Shin, S., Van Dam, R. M., & Finkelstein, E. A. (2020). The Effect of Dynamic Food Labels with Real-Time Feedback on Diet Quality: Results from a Randomized Controlled Trial. Nutrients, 12(7), 2158.

Food systems are a major contributor to global environmental degradation, with dietary choices accounting for roughly 25% of greenhouse gas emissions (Crippa et al., 2021). Despite growing awareness, consumers still face difficulties in identifying more sustainable food options during the shopping process (Camilleri et al., 2019). Even when information is available, the complexity of sustainability indicators and behavioural barriers often prevent this knowledge from translating into action (Carrington et al., 2014).

Online grocery platforms offer new opportunities to support sustainable decision-making through digital tools and interface design. Among these tools, recommender systems are widely used to suggest products based on user behaviour or preferences (de Bauw et al., 2022; Jesse & Jannach, 2021). While often optimised for sales, such systems can also be designed to promote sustainable choices (Felfernig et al., 2023).

This thesis aims to conceptualize a sustainability-sensitive recommender system for online grocery environments. The system could prioritise or highlight products with lower environmental impact, based on indicators such as greenhouse gas emissions, land use, or animal welfare (Clark et al., 2022; Poore & Nemecek, 2018; Ritchie et al., 2022). Students are encouraged to explore or propose their own criteria and recommendation logic. An online experiment should be conducted to evaluate the influence of the recommender system on consumer decision-making. If needed, the system can be implemented as an interactive prototype or mock-up.

The experiment will be implemented in collaboration with researchers from the Nürnberg Institute for Market Decisions (NIM), who will co-supervise the thesis.

Prerequisites:

• Required:
  • Enrolled Master Student in International Information Systems, International Business Studies, Economics, Marketing, etc. Bachelor students can only be considered upon careful consideration of timeline feasibility.
  • Interest in advancing research in the food domain.
  • Willingness to write their thesis in English.
• Highly Desirable:
  • Previous experience in experimental methodology, e.g., through our courses “Experimentelle Verhaltensforschung in Data Science (EVIDS)” (Bachelor) or the seminar “Information Systems for Behavior Change (ISBC)” (Master).
  • Previous knowledge and/or experience in the application of statistical methods (e.g., ANOVA, LMM, etc.)

References

• Camilleri, A. R., Larrick, R. P., Hossain, S., & Patino-Echeverri, D. (2019). Consumers underestimate the emissions associated with food but are aided by labels. Nature Climate Change, 9(1), 53-58.
• Carrington, M. J., Neville, B. A., & Whitwell, G. J. (2014). Lost in translation: Exploring the ethical consumer intention–behavior gap. Journal of business research, 67(1), 2759-2767.
• Clark, M., Springmann, M., Rayner, M., Scarborough, P., Hill, J., Tilman, D., … & Harrington, R. A. (2022). Estimating the environmental impacts of 57,000 food products. Proceedings of the National Academy of Sciences, 119(33), e2120584119.
• Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F. N., & Leip, A. J. N. F. (2021). Food systems are responsible for a third of global anthropogenic GHG emissions. Nature food, 2(3), 198-209.
• De Bauw, M., De La Revilla, L. S., Poppe, V., Matthys, C., & Vranken, L. (2022). Digital nudges to stimulate healthy and pro-environmental food choices in E-groceries. Appetite, 172, 105971.
• Felfernig, A., Wundara, M., Tran, T. N. T., Polat-Erdeniz, S., Lubos, S., El Mansi, M., … & Le, V. M. (2023). Recommender systems for sustainability: overview and research issues. Frontiers in big Data, 6, 1284511.
• Jesse, M., & Jannach, D. (2021). Digital nudging with recommender systems: Survey and future directions. Computers in Human Behavior Reports, 3, 100052. https://doi.org/10.1016/j.chbr.2020.100052
• Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987-992.
• Ritchie, H., Rosado, P., & Roser, M. (2022). Environmental Impacts of Food Production. Environmental Impacts of Food Production. https://ourworldindata.org/environmental-impacts-of-food

Generative Artificial Intelligence (GenAI) has rapidly become pervasive in our everyday lives. However, seminal studies are already showing a range of negative side-effects of GenAI use on cognitive engagement and abilities, risking cognitive atrophy and overreliance (Kosmyna et al., 2025; Lee et al., 2025; Schoeffer et al., 2025; Zhai et al., 2024). Therefore, it is imperative that human-AI interfaces are designed in a way that promotes deep engagement and the critical reflection of outputs (Yatani et al., 2024).

This thesis is meant to contribute to this endeavor by developing and experimentally testing a design intervention in a (mock-up) LLM interaction interface like ChatGPT that promotes reflection and critical thinking. Students may contribute their own specific ideas for study contexts and settings. Possible interventions could be elements that induce deliberate friction, increase transparency or provide metacognitive scaffolds.

The experiment will be implemented in collaboration with researchers from the Nürnberg Institute for Market Decisions (NIM), who will co-supervise the thesis.

Prerequisites:

• Required:
  • Enrolled Master Student in International Information Systems, International Business Studies, Economics, Marketing, etc. Bachelor students can only be considered upon careful consideration of timeline feasibility.
  • Interest in advancing research in the food domain.
  • Willingness to write their thesis in English.
• Highly Desirable:
  • Previous experience in experimental methodology, e.g., through our courses “Experimentelle Verhaltensforschung in Data Science (EVIDS)” (Bachelor) or the seminar “Information Systems for Behavior Change (ISBC)” (Master).
  • Previous knowledge and/or experience in the application of statistical methods (e.g., ANOVA, LMM, etc.)

References

• Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2506.08872

• Lee, H.-P. (Hank), Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–22. https://doi.org/10.1145/3706598.3713778

• Schoeffer, J., Jakubik, J., Vossing, M., Kuhl, N., & Satzger, G. (2025). AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions. Journal of Artificial Intelligence Research, 82, 471–501.

• Yatani, K., Sramek, Z., & Yang, C.-L. (2024). AI as Extraherics: Fostering Higher-order Thinking Skills in Human-AI Interaction (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2409.09218

• Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7

Generative Artificial Intelligence (GenAI) has rapidly become pervasive in our everyday lives. However, seminal studies are already showing a range of negative side-effects of GenAI use on user decision-making, risking cognitive atrophy and overreliance (Kosmyna et al., 2025; Lee et al., 2025; Schoeffer et al., 2025; Zhai et al., 2024). However, studies have also shown that carefully designed human-AI interfaces can mitigate such effects and promote deep engagement and the critical reflection of outputs (Yatani et al., 2024).

In this highly relevant emerging research field, an overview of evidence on GenAI overreliance is necessary. The aim of this thesis is to conduct a literature review to investigate which factors can increase or decrease the occurrence of overreliance in human-(Gen)AI interactions. Students may contribute their own ideas to adjusting the scope.

Prerequisites:

Enrolled student at WiSo. If your study program is not a part of WiSo, please check your program requirements whether our team is allowed to supervise your thesis.

References:

• Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2506.08872
• Lee, H.-P. (Hank), Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–22. https://doi.org/10.1145/3706598.3713778
• Schoeffer, J., Jakubik, J., Vossing, M., Kuhl, N., & Satzger, G. (2025). AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions. Journal of Artificial Intelligence Research, 82, 471–501.
• Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7

Applications are closed for summer semester 2025.

Background

According to the extended mind hypothesis, human cognition extends beyond the brain and nervous system to the body and environmental tools (Clark & Chalmers, 1998). Using technological tools to facilitate cognitive processes is often referred to as “cognitive offloading.” Or “distributed cognition” (Risko & Gilbert, 2016). In the age of generative AI and its increasing capabilities, the possibilities for humans to offload energy- and time-consuming cognitive processes to AI are numerous and evolving rapidly.

Research Gap

In many human-AI collaboration scenarios, humans remain the “final authority” to accept or reject AI recommendations. Therefore, using AI for task completion not only necessitates controlling and monitoring one’s own cognitive processes (often referred to as metacognition) but also evaluating AI’s processes and outputs (Dunn et al., 2021; Tankelevitch et al., 2024). Despite the increasing metacognitive demands of generative AI and the relevance of accurate metacognition to prevent under-/overreliance on AI outputs, there is a lack of research investigating the role of human metacognition in (successful) Human-AI collaborations.

Thesis Goals

Bachelor’s and Master’s theses on this topic aim to investigate the dual role of metacognition: first, in the decision-making process of when to offload cognitive tasks to AI; and second, in how humans evaluate AI outputs once collaboration occurs. Research should explore how targeted interventions can be designed to enhance metacognitive accuracy during human-AI collaboration. The goal is to gain conceptual and empirical insights that will advance our understanding of human-AI cognitive partnerships and inform future design approaches.

Research Approaches

• (Systematic) Literature Review
  • Conduct a (systematic) literature search exploring interventions (such as cognitive forcing strategies) that support metacognitive monitoring/control and prevent excessive cognitive offloading.
  • Review interdisciplinary research and map findings onto generative AI applications.
  • Identify theoretical frameworks that can explain metacognitive processes in human-AI collaboration.
• Experimental Approaches
  • Design creative interventions to support human metacognition before/while using AI and develop experimental protocols to test their effectiveness.
  • Investigate the impacts of using generative AI on human metacognition and cognition(e.g., decision-making, critical thinking, problem-solving).
  • Explore how different AI interface designs (e.g. openAI’s reasoning model) affect users’ metacognitive accuracy.
• Surveys and Interviews
  • Conduct surveys or interviews to investigate factors that determine whether individuals offload cognitive tasks to AI.
  • Explore positive and negative consequences of cognitive offloading to AI.
  • Study domain-specific differences in metacognitive strategies when collaborating with AI.

Starting Date

from August 2025, at least ~6 (Bachelor) / ~8 (Master) months before you plan to hand in and submit your thesis.

Application

Please send a detailed application including your specific topic idea, your CV and transcript of records. For students who are NOT studying WiWi/WINF: Please clarify in advance with the respective degree programme coordinator whether supervision by the chair is permitted. For example, this is often not possible for students from other faculties (TechFak, NatFak).

Requirements

• Interest in interdisciplinary research combining cognitive psychology and AI.
• Basic understanding of experimental design or qualitative research methods.
• Willingness to engage with both technical and psychological literature.
• For experimental approaches: Basic programming skills are beneficial.

References

Clark, A., & Chalmers, D. (1998). The Extended Mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7

Dunn, T. L., Gaspar, C., McLean, D., Koehler, D. J., & Risko, E. F. (2021). Distributed metacognition: Increased Bias and Deficits in Metacognitive Sensitivity when Retrieving Information from the Internet. Technology, Mind, and Behavior, 2(3). https://doi.org/10.1037/tmb0000039

Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002

Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024). The Metacognitive Demands and Opportunities of Generative AI. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1–24. https://doi.org/10.1145/3613904.3642902

Background

According to the IPCC (2014), the transport sector is responsible for approximately 14% of global greenhouse gas emissions. Local public transportation is considered a climate-friendly alternative to private cars and can significantly contribute to reducing CO₂ emissions. Digital mobility applications are crucial in this effort, as they provide users with information about timetables, ticket prices, connections, and the latest updates, making it easier to utilize public transportation.However, many older adults, particularly those over the age of 55, face challenges when using these digital applications. Technological barriers, a lack of digital skills, and insufficient incentives often result in limited or no usage of these apps among this age group. This can complicate their use of public transport and negatively affect their travel experience.

Research Gap

Despite the growing importance of digital tools in the mobility sector, there has been little research to date that specifically addresses the perspective of older people on public transport apps. In particular, there is a lack of knowledge about which factors motivate or inhibit this age group to use mobility apps and what influence this has on their decision to switch from car to public transport.

Thesis Goals

The aim of this thesis is to better understand how older adults (ages 55 and up) perceive the use of mobility apps in public transport and how to motivate them to utilize these tools more actively. The thesis will provide practical insights that can serve as a foundation for future research and user-centered app development.

Several methodological approaches can be employed, including:

• (Systematic) Literature Review
  • What does existing research reveal about the motivations of individuals aged 55 and older for using public transport? What role do mobility apps play in the daily lives of this demographic?
• Interviews with older adults
  • How do older users evaluate existing mobility apps? What features are lacking? What incentives would encourage greater app usage? How can we assist those who are not tech-savvy in getting started with these tools?
• Surveys 
  • Under what conditions are individuals aged 55 and older willing to use mobility apps for public transport? What attitudes and experiences influence their behavior? What factors impact their acceptance of these technologies?

Starting Date

As of now.

Requirements

Good German language skills are desirable for data collection (e.g. survey or interviews).

Application

Please send a detailed application including your specific topic idea, your CV and transcript of records. For students who are NOT studying WiWi/WINF: Please clarify in advance with the respective degree programme coordinator whether supervision by the chair is permitted. For example, this is often not possible for students from other faculties (TechFak, NatFak).

References

IPCC (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Retrieved from <http://www.ipcc.ch/pdf/assessment-report/ar5/syr/AR5_SYR_FINAL_All_Topics.pdf>.

Food systems are a major contributor to global environmental degradation, with dietary choices accounting for roughly 25% of greenhouse gas emissions (Crippa et al., 2021). Despite growing awareness, consumers still struggle to assess the sustainability of food products at the point of sale (Camilleri et al., 2019). This is partly due to the complexity of interpreting sustainability labels and partly due to behavioural barriers. Even when information is available, the intention-behaviour gap often persists (Carrington et al., 2014).

The rise of online grocery shopping allows consumers to make food choices from home (GroceryDoppio, 2023) and opens new opportunities to support more sustainable decisions through targeted interface design (De Bauw et al., 2022). However, real-time sustainability feedback, a potentially powerful intervention, remains underexplored in digital food environments (Shin et al., 2020).

This thesis aims to conceptualize an intervention that combines product-level sustainability information (Clark et al., 2022; Poore & Nemecek, 2018; Ritchie et al., 2022) with real-time, basket-level feedback in an online grocery setting. For example, shoppers could be shown a composite score summarising the overall environmental impact of their basket, based on criteria such as greenhouse gas emissions, land use, or animal welfare. An online experiment should be conducted to test the influence of this feedback on consumer decision-making. If needed, the intervention can be implemented as an interactive prototype or mock-up.

The experiment will be implemented in collaboration with researchers from the Nürnberg Institute for Market Decisions (NIM), who will co-supervise the thesis.

Prerequisites:

• Required:
  • Enrolled Master Student in International Information Systems, International Business Studies, Economics, Marketing, etc. Bachelor students can only be considered upon careful consideration of timeline feasibility.
  • Interest in advancing research in the food domain.
  • Willingness to write their thesis in English.
• Highly Desirable:
  • Previous experience in experimental methodology, e.g., through our courses “Experimentelle Verhaltensforschung in Data Science (EVIDS)” (Bachelor) or the seminar “Information Systems for Behavior Change (ISBC)” (Master).
  • Previous knowledge and/or experience in the application of statistical methods (e.g., ANOVA, LMM, etc.)

References

• Camilleri, A. R., Larrick, R. P., Hossain, S., & Patino-Echeverri, D. (2019). Consumers underestimate the emissions associated with food but are aided by labels. Nature Climate Change, 9(1), 53-58.
• Carrington, M. J., Neville, B. A., & Whitwell, G. J. (2014). Lost in translation: Exploring the ethical consumer intention–behavior gap. Journal of business research, 67(1), 2759-2767.
• Clark, M., Springmann, M., Rayner, M., Scarborough, P., Hill, J., Tilman, D., … & Harrington, R. A. (2022). Estimating the environmental impacts of 57,000 food products. Proceedings of the National Academy of Sciences, 119(33), e2120584119.
• Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F. N., & Leip, A. J. N. F. (2021). Food systems are responsible for a third of global anthropogenic GHG emissions. Nature food, 2(3), 198-209.
• De Bauw, M., De La Revilla, L. S., Poppe, V., Matthys, C., & Vranken, L. (2022). Digital nudges to stimulate healthy and pro-environmental food choices in E-groceries. Appetite, 172, 105971.
• GroceryDoppio. (2023, February 7). January 2023: State of Digital Grocery Performance Scorecard. Performance Scorecard. https://www.grocerydoppio.com/performance-scorecard/january-2023-state-of-digital-grocery-performance-scorecard
• Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987-992.
• Ritchie, H., Rosado, P., & Roser, M. (2022). Environmental Impacts of Food Production. Environmental Impacts of Food Production. https://ourworldindata.org/environmental-impacts-of-food
• Shin, S., Van Dam, R. M., & Finkelstein, E. A. (2020). The Effect of Dynamic Food Labels with Real-Time Feedback on Diet Quality: Results from a Randomized Controlled Trial. Nutrients, 12(7), 2158.

Food systems are a major contributor to global environmental degradation, with dietary choices accounting for roughly 25% of greenhouse gas emissions (Crippa et al., 2021). Despite growing awareness, consumers still face difficulties in identifying more sustainable food options during the shopping process (Camilleri et al., 2019). Even when information is available, the complexity of sustainability indicators and behavioural barriers often prevent this knowledge from translating into action (Carrington et al., 2014).

Online grocery platforms offer new opportunities to support sustainable decision-making through digital tools and interface design. Among these tools, recommender systems are widely used to suggest products based on user behaviour or preferences (de Bauw et al., 2022; Jesse & Jannach, 2021). While often optimised for sales, such systems can also be designed to promote sustainable choices (Felfernig et al., 2023).

This thesis aims to conceptualize a sustainability-sensitive recommender system for online grocery environments. The system could prioritise or highlight products with lower environmental impact, based on indicators such as greenhouse gas emissions, land use, or animal welfare (Clark et al., 2022; Poore & Nemecek, 2018; Ritchie et al., 2022). Students are encouraged to explore or propose their own criteria and recommendation logic. An online experiment should be conducted to evaluate the influence of the recommender system on consumer decision-making. If needed, the system can be implemented as an interactive prototype or mock-up.

The experiment will be implemented in collaboration with researchers from the Nürnberg Institute for Market Decisions (NIM), who will co-supervise the thesis.

Prerequisites:

• Required:
  • Enrolled Master Student in International Information Systems, International Business Studies, Economics, Marketing, etc. Bachelor students can only be considered upon careful consideration of timeline feasibility.
  • Interest in advancing research in the food domain.
  • Willingness to write their thesis in English.
• Highly Desirable:
  • Previous experience in experimental methodology, e.g., through our courses “Experimentelle Verhaltensforschung in Data Science (EVIDS)” (Bachelor) or the seminar “Information Systems for Behavior Change (ISBC)” (Master).
  • Previous knowledge and/or experience in the application of statistical methods (e.g., ANOVA, LMM, etc.)

References

• Camilleri, A. R., Larrick, R. P., Hossain, S., & Patino-Echeverri, D. (2019). Consumers underestimate the emissions associated with food but are aided by labels. Nature Climate Change, 9(1), 53-58.
• Carrington, M. J., Neville, B. A., & Whitwell, G. J. (2014). Lost in translation: Exploring the ethical consumer intention–behavior gap. Journal of business research, 67(1), 2759-2767.
• Clark, M., Springmann, M., Rayner, M., Scarborough, P., Hill, J., Tilman, D., … & Harrington, R. A. (2022). Estimating the environmental impacts of 57,000 food products. Proceedings of the National Academy of Sciences, 119(33), e2120584119.
• Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F. N., & Leip, A. J. N. F. (2021). Food systems are responsible for a third of global anthropogenic GHG emissions. Nature food, 2(3), 198-209.
• De Bauw, M., De La Revilla, L. S., Poppe, V., Matthys, C., & Vranken, L. (2022). Digital nudges to stimulate healthy and pro-environmental food choices in E-groceries. Appetite, 172, 105971.
• Felfernig, A., Wundara, M., Tran, T. N. T., Polat-Erdeniz, S., Lubos, S., El Mansi, M., … & Le, V. M. (2023). Recommender systems for sustainability: overview and research issues. Frontiers in big Data, 6, 1284511.
• Jesse, M., & Jannach, D. (2021). Digital nudging with recommender systems: Survey and future directions. Computers in Human Behavior Reports, 3, 100052. https://doi.org/10.1016/j.chbr.2020.100052
• Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987-992.
• Ritchie, H., Rosado, P., & Roser, M. (2022). Environmental Impacts of Food Production. Environmental Impacts of Food Production. https://ourworldindata.org/environmental-impacts-of-food

Generative Artificial Intelligence (GenAI) has rapidly become pervasive in our everyday lives. However, seminal studies are already showing a range of negative side-effects of GenAI use on cognitive engagement and abilities, risking cognitive atrophy and overreliance (Kosmyna et al., 2025; Lee et al., 2025; Schoeffer et al., 2025; Zhai et al., 2024). Therefore, it is imperative that human-AI interfaces are designed in a way that promotes deep engagement and the critical reflection of outputs (Yatani et al., 2024).

This thesis is meant to contribute to this endeavor by developing and experimentally testing a design intervention in a (mock-up) LLM interaction interface like ChatGPT that promotes reflection and critical thinking. Students may contribute their own specific ideas for study contexts and settings. Possible interventions could be elements that induce deliberate friction, increase transparency or provide metacognitive scaffolds.

The experiment will be implemented in collaboration with researchers from the Nürnberg Institute for Market Decisions (NIM), who will co-supervise the thesis.

Prerequisites:

• Required:
  • Enrolled Master Student in International Information Systems, International Business Studies, Economics, Marketing, etc. Bachelor students can only be considered upon careful consideration of timeline feasibility.
  • Interest in advancing research in the food domain.
  • Willingness to write their thesis in English.
• Highly Desirable:
  • Previous experience in experimental methodology, e.g., through our courses “Experimentelle Verhaltensforschung in Data Science (EVIDS)” (Bachelor) or the seminar “Information Systems for Behavior Change (ISBC)” (Master).
  • Previous knowledge and/or experience in the application of statistical methods (e.g., ANOVA, LMM, etc.)

References

• Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2506.08872

• Lee, H.-P. (Hank), Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–22. https://doi.org/10.1145/3706598.3713778

• Schoeffer, J., Jakubik, J., Vossing, M., Kuhl, N., & Satzger, G. (2025). AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions. Journal of Artificial Intelligence Research, 82, 471–501.

• Yatani, K., Sramek, Z., & Yang, C.-L. (2024). AI as Extraherics: Fostering Higher-order Thinking Skills in Human-AI Interaction (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2409.09218

• Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7

Generative Artificial Intelligence (GenAI) has rapidly become pervasive in our everyday lives. However, seminal studies are already showing a range of negative side-effects of GenAI use on user decision-making, risking cognitive atrophy and overreliance (Kosmyna et al., 2025; Lee et al., 2025; Schoeffer et al., 2025; Zhai et al., 2024). However, studies have also shown that carefully designed human-AI interfaces can mitigate such effects and promote deep engagement and the critical reflection of outputs (Yatani et al., 2024).

In this highly relevant emerging research field, an overview of evidence on GenAI overreliance is necessary. The aim of this thesis is to conduct a literature review to investigate which factors can increase or decrease the occurrence of overreliance in human-(Gen)AI interactions. Students may contribute their own ideas to adjusting the scope.

Prerequisites:

Enrolled student at WiSo. If your study program is not a part of WiSo, please check your program requirements whether our team is allowed to supervise your thesis.

References:

• Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2506.08872
• Lee, H.-P. (Hank), Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–22. https://doi.org/10.1145/3706598.3713778
• Schoeffer, J., Jakubik, J., Vossing, M., Kuhl, N., & Satzger, G. (2025). AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions. Journal of Artificial Intelligence Research, 82, 471–501.
• Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7

Background

According to the extended mind hypothesis, human cognition extends beyond the brain and nervous system to the body and environmental tools (Clark & Chalmers, 1998). Using technological tools to facilitate cognitive processes is often referred to as “cognitive offloading.” Or “distributed cognition” (Risko & Gilbert, 2016). In the age of generative AI and its increasing capabilities, the possibilities for humans to offload energy- and time-consuming cognitive processes to AI are numerous and evolving rapidly.

Research Gap

In many human-AI collaboration scenarios, humans remain the “final authority” to accept or reject AI recommendations. Therefore, using AI for task completion not only necessitates controlling and monitoring one’s own cognitive processes (often referred to as metacognition) but also evaluating AI’s processes and outputs (Dunn et al., 2021; Tankelevitch et al., 2024). Despite the increasing metacognitive demands of generative AI and the relevance of accurate metacognition to prevent under-/overreliance on AI outputs, there is a lack of research investigating the role of human metacognition in (successful) Human-AI collaborations.

Thesis Goals

Bachelor’s and Master’s theses on this topic aim to investigate the dual role of metacognition: first, in the decision-making process of when to offload cognitive tasks to AI; and second, in how humans evaluate AI outputs once collaboration occurs. Research should explore how targeted interventions can be designed to enhance metacognitive accuracy during human-AI collaboration. The goal is to gain conceptual and empirical insights that will advance our understanding of human-AI cognitive partnerships and inform future design approaches.

Research Approaches

• (Systematic) Literature Review
  • Conduct a (systematic) literature search exploring interventions (such as cognitive forcing strategies) that support metacognitive monitoring/control and prevent excessive cognitive offloading.
  • Review interdisciplinary research and map findings onto generative AI applications.
  • Identify theoretical frameworks that can explain metacognitive processes in human-AI collaboration.
• Experimental Approaches
  • Design creative interventions to support human metacognition before/while using AI and develop experimental protocols to test their effectiveness.
  • Investigate the impacts of using generative AI on human metacognition and cognition(e.g., decision-making, critical thinking, problem-solving).
  • Explore how different AI interface designs (e.g. openAI’s reasoning model) affect users’ metacognitive accuracy.
• Surveys and Interviews
  • Conduct surveys or interviews to investigate factors that determine whether individuals offload cognitive tasks to AI.
  • Explore positive and negative consequences of cognitive offloading to AI.
  • Study domain-specific differences in metacognitive strategies when collaborating with AI.

Starting Date

from August 2025, at least ~6 (Bachelor) / ~8 (Master) months before you plan to hand in and submit your thesis.

Application

Please send a detailed application including your specific topic idea, your CV and transcript of records. For students who are NOT studying WiWi/WINF: Please clarify in advance with the respective degree programme coordinator whether supervision by the chair is permitted. For example, this is often not possible for students from other faculties (TechFak, NatFak).

Requirements

• Interest in interdisciplinary research combining cognitive psychology and AI.
• Basic understanding of experimental design or qualitative research methods.
• Willingness to engage with both technical and psychological literature.
• For experimental approaches: Basic programming skills are beneficial.

References

Clark, A., & Chalmers, D. (1998). The Extended Mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7

Dunn, T. L., Gaspar, C., McLean, D., Koehler, D. J., & Risko, E. F. (2021). Distributed metacognition: Increased Bias and Deficits in Metacognitive Sensitivity when Retrieving Information from the Internet. Technology, Mind, and Behavior, 2(3). https://doi.org/10.1037/tmb0000039

Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002

Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024). The Metacognitive Demands and Opportunities of Generative AI. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1–24. https://doi.org/10.1145/3613904.3642902

Background

According to the IPCC (2014), the transport sector is responsible for approximately 14% of global greenhouse gas emissions. Local public transportation is considered a climate-friendly alternative to private cars and can significantly contribute to reducing CO₂ emissions. Digital mobility applications are crucial in this effort, as they provide users with information about timetables, ticket prices, connections, and the latest updates, making it easier to utilize public transportation.However, many older adults, particularly those over the age of 55, face challenges when using these digital applications. Technological barriers, a lack of digital skills, and insufficient incentives often result in limited or no usage of these apps among this age group. This can complicate their use of public transport and negatively affect their travel experience.

Research Gap

Despite the growing importance of digital tools in the mobility sector, there has been little research to date that specifically addresses the perspective of older people on public transport apps. In particular, there is a lack of knowledge about which factors motivate or inhibit this age group to use mobility apps and what influence this has on their decision to switch from car to public transport.

Thesis Goals

The aim of this thesis is to better understand how older adults (ages 55 and up) perceive the use of mobility apps in public transport and how to motivate them to utilize these tools more actively. The thesis will provide practical insights that can serve as a foundation for future research and user-centered app development.

Several methodological approaches can be employed, including:

• (Systematic) Literature Review
  • What does existing research reveal about the motivations of individuals aged 55 and older for using public transport? What role do mobility apps play in the daily lives of this demographic?
• Interviews with older adults
  • How do older users evaluate existing mobility apps? What features are lacking? What incentives would encourage greater app usage? How can we assist those who are not tech-savvy in getting started with these tools?
• Surveys 
  • Under what conditions are individuals aged 55 and older willing to use mobility apps for public transport? What attitudes and experiences influence their behavior? What factors impact their acceptance of these technologies?

Starting Date

As of now.

Requirements

Good German language skills are desirable for data collection (e.g. survey or interviews).

Application

Please send a detailed application including your specific topic idea, your CV and transcript of records. For students who are NOT studying WiWi/WINF: Please clarify in advance with the respective degree programme coordinator whether supervision by the chair is permitted. For example, this is often not possible for students from other faculties (TechFak, NatFak).

References

IPCC (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Retrieved from <http://www.ipcc.ch/pdf/assessment-report/ar5/syr/AR5_SYR_FINAL_All_Topics.pdf>.