AI in Research, Research in AI
At BAU, researchers use AI to accelerate scientific discovery, make sense of data, and design solutions that create social value. At the same time, they research how AI itself can be developed in a trustworthy, explainable, ethical and sustainable way.
- 8 priority areas
- Interdisciplinary R&D
- Responsible and ethical research
We are strengthening research capacity with AI
BAU uses AI as a generative tool that deepens research questions, accelerates interdisciplinary collaboration, and helps translate scientific output into social impact.
BAU Priority Research, R&D and Innovation Areas
AI is positioned as a horizontal priority area that intersects with, strengthens, and expands the capacity of all of BAU's vertical research areas. This approach helps knowledge produced across different disciplines translate into higher impact through data science, smart systems and digital technologies.
Energetic Technologies: Energy Systems of the Future
Renewable energy sources, energy storage and transmission systems, sustainable energy technologies.
Smart Manufacturing and Materials Science
Nanotechnology, advanced manufacturing techniques, smart materials, Industry 4.0.
The Future of Health: Biotechnology and Digital Health
Personalized medicine, biotech drugs, drug design, genomics, digital health platforms, neuroscience and mental-health technologies.
Next-Generation Finance and Economics
Digital finance, FinTech, sustainable economy, innovation management.
A Sustainable Future: Green Technologies and the Circular Economy
Renewable energy, carbon capture and storage, water and waste management, smart cities.
Digital Evolution: The Future of Culture, Society and Media
Digital media, creative industries, social media analytics, strategic communication, cultural studies and human behavior in the digital age.
Advanced Education Paradigms: AI, Data and Personalization
Digital education platforms, AI-supported education, data analytics in education.
AI Ethics, Regulation and Human Rights Law
Ethical use, data privacy, digital rights, mitigation of algorithmic bias, regulatory technologies, and the relationship between human rights law and AI.
The Digital Age: AI, Smart Systems and Data Science
AI, data science, big data analytics, cybersecurity, the Internet of Things and the Internet of Everything, robotics and automation — the horizontal axis that cuts across and strengthens every priority area.
How is AI used in research?
For BAU researchers, AI is not only a text-generating tool but is also used as a supportive working partner at different stages of the process — from clarifying the research question to literature mapping, from method design to data analysis, from manuscript preparation to reviewer responses.
Literature mapping
Quickly make sense of a field's foundational publications, concepts, methods and gaps; make a systematic start on manuscript and review preparation.
Research question and hypothesis
Explore new topics, narrow the problem space, assess the potential for original contribution, and strengthen the scientific framework.
Method and analysis planning
Compare methods suited to the data and the aim, plan the analysis steps, and review the study design.
Coding and computation support
Draft data preprocessing, computation checks, visualization and analysis workflows with tools such as Python and MATLAB.
Manuscript drafting and academic writing
Build the manuscript skeleton, organize the methods and findings sections, and make the writing meet academic standards while staying easy to read.
Reviewer responses and revision
Classify reviewer comments, develop a response strategy, prepare a revision plan, and strengthen the scientific argument.
Project and grant applications
Turn a research idea into a project structure, clarify the aim–objective–output relationship, and strengthen the work packages and impact narrative.
Team research productivity
Support shared reading, analysis, note-taking and draft-production processes for thesis students and interdisciplinary teams.
AI outputs do not replace the researcher's scientific responsibility. All outputs must be verified by a domain expert; sources, data, methods and ethical compliance must be checked under human oversight.
Responsible use of AI in research
At BAU, the use of AI in research processes is evaluated together with the principles of academic integrity, data security, KVKK, intellectual and industrial property, source accuracy, human oversight and scientific responsibility. Researchers are expected to verify generated outputs, check sources, pay attention to data privacy and act in accordance with ethical rules.
Decision and responsibility rest with the researcher
AI provides speed, structure and analysis support during the research process. However, the scientific decision, the choice of method, the final interpretation and the responsibility for publication always belong to the researcher.
Collaborations and impact
BAU treats AI research both as academic output and as impact-focused solutions developed together with industry, public institutions, the entrepreneurship ecosystem and international partners.
University–industry collaboration
Turning sector problems into research questions, joint R&D projects, and applied AI solutions.
Entrepreneurship and commercialization
Turning research outputs into ventures, products or licensable technologies with the support of BAU Hub, BUG Lab TEKMER and BAUTTO.
Meet the unitsInternational research networks
Co-production and visibility through global networks of universities, research centers, technology providers and funders.
Do you need AI and grant support for your research?
To turn your research idea into a project, access the right tools, develop collaborations, or get support on ethical and secure use, you can contact the AI Competence Unit and BAUTTO.