alignAI
Aligning LLM Technologies with Societal Values
About alignAI
About the project
The alignAI Doctoral Network will train 17 doctoral candidates (DCs) to work in the international and highly interdisciplinary field of LLM research and development. The core of the project focuses on the alignment of LLMs with human values, identifying relevant values and methods for alignment implementation. Two principles provide a foundation for the approach. First, explainability is a key enabler for all aspects of trustworthiness, accelerating development, promoting usability, and facilitating human oversight and auditing of LLMs. Second, fairness is a key aspect of trustworthiness, facilitating access to AI applications and ensuring equal impact of AI-driven decision-making. The practical relevance of the project is ensured by three use cases in education, positive mental health, and news consumption. This approach allows us to develop specific guidelines and test prototypes and tools to promote value alignment. We follow a unique methodological approach, with DCs from social sciences and humanities “twinned” with DCs from technical disciplines for each use case (9 DCs in total), while the other 8 DCs carry out horizontal research across the use cases.
About Large Language Models
Large Language Models (LLMs) are trained on broad data, using self-supervision at scale, to complete a wide range of tasks. Wider use of LLMs has risen in recent months due to applications such as ChatGPT. Although LLMs bring many opportunities to improve our everyday lives, the impacts on humans and society have not yet been prioritized or fully understood. Given the rapid development of these tools, the risk of negative implications is significant if LLMs are not developed and deployed in a way that is aligned with human values and responds to individual needs and preferences. To mitigate any negative consequences, academia, in close collaboration with industry, needs to train the next generation of researchers to understand the complexities of the socio-technical implications surrounding the use of LLMs.
Project Map
The alignAI project is built around a highly interdisciplinary training program
and research methodology designed to achieve the DN’s five research objectives:
- O1. Establish a unique doctoral training programme (i) equipping DCs with the capacity to work in interdisciplinary environments, (ii) providing high quality scientific training, (iii) equipping DCs with communication capacities and (iv) Disseminating knowledge beyond the beneficiary institutions
- O2. Identify the human values and user requirements/preferences that LLMs should align with
- O3. Explore implementable ways for applying the principles of explainability (XAI) and fairness in
the specific context of LLM use to enable alignment with values identified in RIO1 - O4. Design and build value aligned LLM prototype tools based on outcomes from RIO1 and RIO2
- O5. Test & validate the technical prototype tools from RIO3 and the non-technical
tools/methods/models from RIO1 and RIO2 - O6. Translate learnings from RIO1-RIO4 into research outputs, contextualising an “enabling
environment” for value-aligned LLMs
The doctoral training objective O1 is described in detail in Section 1.3.
The five research objectives will be addressed in a context-specific way throughout the project by
investigating them as part of three use-cases in: (i) Education, (ii) Positive Mental Health and (iii) Online
News Consumption. Fig. 3. presents the proposed research methodology. This is followed by a detailed
description of the research activities and their relevance for the project objectives.
Newsroom
Q&A with PI Carlos Zednik
In this video interview, we speak with Carlos Zednik, Assistant Professor at the Eindhoven University of Technology. He introduces himself and his work, presents his doctoral candidates and their research, and explains what motivated him to join the alignAI project.
Navigating Online Mental Health Information: The Role of Value-aligned AI
OpenAI recently released a new product designed to support medical care: ChatGPT Health. They describe this innovation as an “experience that securely brings your health information and ChatGPT’s intelligence together, to help you feel more informed, prepared and confident navigating your health” (Introducing ChatGPT Health, 2026). They claim that health is already one of the most common ways people use ChatGPT, and that this platform will increase users’ knowledge, preparation and confidence in managing their health (Introducing ChatGPT Health, 2026). However, while AI-powered health tools may reduce information overload and improve accessibility, their integration into help-seeking processes raises significant ethical questions regarding misinformation, autonomy and bias.
When Algorithms Meet the Mind: AI, Mental Health & Ethics
On 7 May 2026, alignAI doctoral candidate Simay Toplu was an invited guest lecturer at Technical University of Munich as part of Prof. Christoph Lütge’s course on Ethics of Artificial Intelligence.
The Oracle Speaks in Declaratives: When Confident Answers Obscure the Need for Interpretation
For decades, human-computer interaction was built on a comfortable assumption: people click, systems respond and the boundaries are clear. A button is a button, a menu is a menu. When things go wrong, you can often spot the missing step, the frozen cursor, the bad modal window.
Value Alignment and Craft in AI Education at CHI 2026 Barcelona
The ACM CHI Conference on Human Factors in Computing Systems took place in Barcelona 13–17 April, 2026. alignAI doctoral candidate Tuan-Ting Huang attended the conference and presented a poster titled “Workmanship of Learning: Embedding Craftsmanship Values in AI-Integrated Educational Tools”, proposing a value-oriented framework for design education in the age of AI.
Understanding the Role of “AI Friends” in Children’s Sociocognitive Development
For years, the industry has focused on making models bigger. This training time scaling (Kaplan et al., 2020) made models highly fluent, similar to a student who memorised the entire textbook. But fluency is not the same as reasoning. Large language models (LLMs) still struggle with complex logic, maths or coding tasks because they respond too quickly, predicting the next word without truly thinking (McCoy et al., 2023).