Open topics

Background

In Germany, the heating supply accounts for over 50 % of total energy consumption and a significant proportion of CO2 emissions. With the ongoing climate crisis, the decarbonisation of the heat supply is a decisive factor for change and the achievement of climate targets. Unused potential lies in particular in the implementation of local heat networks (Joint Research Centre of the European Commission, 2022). A major obstacle to this is the lack of data required for planning, which is typically not available at the beginning of the planning process or is difficult to collect.

Research Gap

Data donations by citizens can help to overcome this challenge. Data donations describe the active sharing of data by individuals, usually to promote public interest (Hartl et al., 2024). Previous research on data donations is largely based on the enquiry of hypothetical donation intentions, i.e. there is a lack of practical applications (Silber et al., 2022). In addition, despite the great potential of this approach for applications in the energy sector (Lee et al., 2024), there is little literature to date that deals with the donation of energy data.

Thesis Goals

Bachelor’s/Master’s theses on this topic are to find out which conditions and challenges need to be taken into account when implementing energy data donations. The aim is to gain practical insights that will pave the way for future research. Various approaches are conceivable, including, but not limited to:

• Literature and internet research: What successful/planned data donation projects are there in the energy sector? What lessons can be learnt from them?
• Research + self-experiment: How can private individuals access their energy/consumption data from energy suppliers? What types of data are available? What is the quality of the data?
• Surveys: Under what circumstances are private individuals willing to donate their energy and/or consumption data for research purposes?
• Expert interviews with
  • Data protection officers: What special features need to be taken into account when storing and sharing energy/consumption data?
  • Energy suppliers/cooperatives: Can data controllers imagine facilitating data donations for (non-commercial) research projects?

Starting Date

from Mai 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).

References

Hartl, P., Hassler, J., Manzke, L., Schmidbauer, E., Schnurr, D., & Tiefenbeck, V. (2024). Data Donations: Data Disclosure for the Common Good. https://doi.org/10.2139/ssrn.4969398

Joint Research Centre of the European Commission. (2022). Towards a green & digital future: Key requirements for successful twin transitions in the European Union. Publications Office. https://data.europa.eu/doi/10.2760/977331

Lee, D., Long, L. A. N., & Jansma, S. R. (2024). Data donation: Using the gift relationship framework to address privacy and environmental issues of energy consumption data collection. Energy Research & Social Science, 114, 103596. https://doi.org/10.1016/j.erss.2024.103596

Silber, H., Breuer, J., Beuthner, C., Gummer, T., Keusch, F., Siegers, P., Stier, S., & Weiß, B. (2022). Linking surveys and digital trace data: Insights from two studies on determinants of data sharing behaviour. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185, S387–S407. https://doi.org/10.1111/rssa.12954

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

Applications are closed for summer semester 2025.

Background

In Germany, the heating supply accounts for over 50 % of total energy consumption and a significant proportion of CO2 emissions. With the ongoing climate crisis, the decarbonisation of the heat supply is a decisive factor for change and the achievement of climate targets. Unused potential lies in particular in the implementation of local heat networks (Joint Research Centre of the European Commission, 2022). A major obstacle to this is the lack of data required for planning, which is typically not available at the beginning of the planning process or is difficult to collect.

Research Gap

Data donations by citizens can help to overcome this challenge. Data donations describe the active sharing of data by individuals, usually to promote public interest (Hartl et al., 2024). Previous research on data donations is largely based on the enquiry of hypothetical donation intentions, i.e. there is a lack of practical applications (Silber et al., 2022). In addition, despite the great potential of this approach for applications in the energy sector (Lee et al., 2024), there is little literature to date that deals with the donation of energy data.

Thesis Goals

Bachelor’s/Master’s theses on this topic are to find out which conditions and challenges need to be taken into account when implementing energy data donations. The aim is to gain practical insights that will pave the way for future research. Various approaches are conceivable, including, but not limited to:

• Literature and internet research: What successful/planned data donation projects are there in the energy sector? What lessons can be learnt from them?
• Research + self-experiment: How can private individuals access their energy/consumption data from energy suppliers? What types of data are available? What is the quality of the data?
• Surveys: Under what circumstances are private individuals willing to donate their energy and/or consumption data for research purposes?
• Expert interviews with
  • Data protection officers: What special features need to be taken into account when storing and sharing energy/consumption data?
  • Energy suppliers/cooperatives: Can data controllers imagine facilitating data donations for (non-commercial) research projects?

Starting Date

from Mai 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).

References

Hartl, P., Hassler, J., Manzke, L., Schmidbauer, E., Schnurr, D., & Tiefenbeck, V. (2024). Data Donations: Data Disclosure for the Common Good. https://doi.org/10.2139/ssrn.4969398

Joint Research Centre of the European Commission. (2022). Towards a green & digital future: Key requirements for successful twin transitions in the European Union. Publications Office. https://data.europa.eu/doi/10.2760/977331

Lee, D., Long, L. A. N., & Jansma, S. R. (2024). Data donation: Using the gift relationship framework to address privacy and environmental issues of energy consumption data collection. Energy Research & Social Science, 114, 103596. https://doi.org/10.1016/j.erss.2024.103596

Silber, H., Breuer, J., Beuthner, C., Gummer, T., Keusch, F., Siegers, P., Stier, S., & Weiß, B. (2022). Linking surveys and digital trace data: Insights from two studies on determinants of data sharing behaviour. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185, S387–S407. https://doi.org/10.1111/rssa.12954

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