When Does AI Improve Human Decision Making, and When Does It Undermine Independent Judgement?

Host institution: Faculty of Management, Prague University of Economics and Business

Location: Jindřichův Hradec, Czech Republic

Supervisor: doc. Ing. Tomáš Kincl, Ph.D.

Funding scheme: Horizon Europe, Marie Skłodowska-Curie Actions, Postdoctoral Fellowships 2026

Fellowship type: European Postdoctoral Fellowship, 12–24 months

Expression of Interest deadline: 24 August 2026

MSCA-PF 2026 deadline: 9 September 2026

Contact: kincl@vse.cz

Expressions of interest will be reviewed on a rolling basis. Early contact is strongly encouraged.

Research opportunity

Artificial intelligence, particularly generative AI, is rapidly becoming part of professional, educational and everyday decision-making. AI systems can provide information, generate alternatives, reduce routine cognitive work and support complex decisions. At the same time, increasing reliance on AI raises a fundamental question: when does AI genuinely improve human judgement, and when does it weaken people's ability to evaluate information and make decisions independently?

Existing research often focuses on AI acceptance, intention to use, perceived usefulness or short-term task performance. These perspectives provide only a partial account of human-AI interaction. High-performing AI assistance may improve an immediate decision while simultaneously reducing information verification, cognitive effort, error detection or retention of expertise.

Research at the host institution has already identified this tension. Recent work on Responsible AI and knowledge creation points to productivity gains alongside risks such as misinformation, bias and reduced human cognitive engagement. Research on GenAI use has also identified epistemic vigilance, cognitive engagement, ethical dilemmas and deskilling anxiety as issues that conventional technology-adoption models do not adequately capture.

We are looking for a postdoctoral researcher interested in developing an original MSCA project around the broader problem of human judgement under AI assistance.

The fellowship should go beyond asking whether people trust, accept or intend to use AI. We are particularly interested in explaining the mechanisms and boundary conditions that determine whether AI acts as a form of cognitive augmentation or instead leads to overreliance, cognitive offloading and deterioration of independent judgement.

Possible research directions include:

• calibration of trust in AI and the conditions leading to overtrust or undertrust;
• automation bias and reliance on incorrect or misleading AI recommendations;
• cognitive offloading and its consequences for reasoning, learning and retention of expertise;
• epistemic vigilance and people's willingness and ability to verify AI-generated information;
• human-AI disagreement and the conditions under which people challenge or defer to AI;
• effects of AI confidence, uncertainty communication and explanations on human judgement;
• the relationship between AI assistance, cognitive effort and decision quality;
• repeated or longitudinal AI use and the development or erosion of independent competence;
• individual differences in susceptibility to AI reliance;
• interventions and interface designs that support effective human-AI collaboration while preserving meaningful human oversight.

These directions are not a predetermined project design. The final research questions, theoretical framework, application context and methodology will be developed jointly with the selected researcher and should reflect their expertise and career objectives.

Why this research matters

The central question is broader than whether AI can outperform humans on individual tasks. Organisations and societies increasingly need to understand what happens to human capability when decision-making is repeatedly supported by AI.

A system can increase immediate task performance while producing undesirable longer-term consequences if users become less able to detect errors, assess evidence, justify decisions or perform the task independently. Conversely, poorly designed attempts to maintain human control may prevent users from benefiting from genuinely useful AI assistance.

The central scientific problem can therefore be formulated as:

Under what conditions does AI augment human judgement while preserving the cognitive capabilities required for independent and responsible decision-making?

Answering this question can connect research in behavioural science, cognitive psychology, human-computer interaction, information systems and management. It can also contribute to the emerging discussion on responsible AI by shifting attention from the characteristics of AI systems alone towards the dynamic relationship between AI assistance and human competence.

The project can have direct societal and organisational relevance. Employers, educators, professionals and policymakers need evidence on how AI-supported decision systems should be designed and used without creating inappropriate dependence on automated recommendations.

Possible application contexts

The fellowship is intentionally not restricted to one empirical domain. The final context should follow from the selected fellow's expertise and the research question.

Possible contexts include:

• managerial and organisational decision-making;
• professional knowledge work;
• consumer decision-making;
• education and learning;
• sustainability-related decisions, including situations in which AI helps individuals or managers evaluate competing environmental, economic and social considerations;
• information evaluation and misinformation detection;
• other high-relevance contexts in which humans increasingly rely on AI-generated information or recommendations.

Sustainability can therefore serve as one possible lens or application domain rather than a required component of the fellowship. For example, a project could examine whether AI assistance improves genuinely sustainable decisions, changes how people process competing sustainability claims, or simply increases confidence in decisions without improving their quality.

Who we are looking for

We welcome researchers whose expertise complements the supervisor's background in consumer behaviour, technology adoption, digital behaviour, management and research methodology.

Relevant disciplinary backgrounds may include cognitive psychology, behavioural science, human-computer interaction, information systems, decision science, management, computational social science, consumer psychology, educational psychology, or closely related fields.

Previous publications specifically on generative AI are welcome but not required.

We are particularly interested in candidates who can bring theoretical or methodological expertise such as:

• controlled behavioural experiments;
• experimental psychology;
• causal inference;
• longitudinal or repeated-measures designs;
• human-computer interaction experiments;
• behavioural measurement beyond self-reported intentions;
• computational methods;
• process measures of information search or verification;
• advanced quantitative methods;
• methods for studying learning, cognitive effort, judgement or expertise development.

The strongest candidate will bring expertise that complements rather than duplicates the capabilities already available at the host. This is important because MSCA-PF explicitly evaluates the quality of supervision, training and the two-way transfer of knowledge between the researcher and the host organisation.

Research environment

The fellowship will be hosted by the Faculty of Management, Prague University of Economics and Business, an AACSB-accredited university. The fellow will not be required to remain within an existing research group. Depending on the direction of the fellowship, they may develop a dedicated research stream, research group or laboratory around human-AI judgement and decision-making.

Research infrastructure available at the Faculty includes a marketing research laboratory equipped with Tobii eye-tracking technology, which may support studies of attention, information processing and decision behaviour where relevant to the final design. The Faculty also provides an active doctoral and research environment.

What the host offers

The selected fellow will have the opportunity to:

• work closely with the supervisor on the development and implementation of an individual research programme in human-AI judgement;
• participate in the Faculty's doctoral programme, doctoral seminars and research seminars;
• contribute to co-supervision and mentoring of doctoral researchers, where relevant to the fellow's expertise;
• establish their own research stream, research group or specialised lab associated with the fellowship;
• collaborate with researchers studying innovation adoption, behavioural decision-making, generative AI, consumer behaviour and sustainability;
• use the Faculty's experimental and behavioural research infrastructure;
• develop new methodological skills and exchange methodological expertise with researchers at the host institution;
• contribute to teaching where this supports the fellow's career development and is compatible with the fellowship;
• develop international publications and conference contributions with support from the host;
• receive institutional support during preparation of the MSCA-PF proposal.

Depending on the final project design, a secondment or non-academic placement may also be incorporated where it provides clear scientific and career-development value. A European Postdoctoral Fellowship may include an additional non-academic placement of up to six months at the end of the fellowship.

Supervisor

doc. Ing. Tomáš Kincl, Ph.D. is an Associate Professor at the Faculty of Management, Prague University of Economics and Business. His research covers consumer and digital behaviour, technology adoption, emerging technologies, corporate communication and research methodology.

His recent research is directly relevant to the proposed fellowship. A 2026 study published in Technological Forecasting and Social Change examines the opportunities and risks created by generative AI in knowledge creation, including productivity improvements, misinformation, bias, privacy risks and reduced human cognitive engagement.

Another recent international study examines university students' perceptions of GenAI and identifies limitations of conventional technology-adoption models in explaining AI use. The research highlights epistemic vigilance, cognitive engagement, institutional legitimacy and concerns about deskilling and future expertise.

His broader research includes digital behaviour and technology-related wellbeing, consumer decision-making, responsible consumption and cross-cultural digital behaviour. Previous work has examined digital generations using data from more than 57,000 respondents across 44 countries.

His publications include articles in Technological Forecasting and Social Change, Journal of Retailing and Consumer Services, Behaviour & Information Technology, Computer Speech & Language, Corporate Communications and other international journals.

He has supervised five successfully completed PhD candidates, currently supervises doctoral researchers, and has experience mentoring postdoctoral researchers.

The supervisor will contribute expertise in technology adoption and digital behaviour, consumer and managerial decision-making, research design, quantitative and mixed methods, and academic publication. The selected fellow is expected to contribute complementary theoretical or methodological expertise, particularly in areas such as cognitive or experimental psychology, HCI, causal methods, behavioural experimentation or computational approaches.

Eligibility

Candidates should carefully verify their eligibility for the MSCA European Postdoctoral Fellowship 2026.

In particular:

• applicants may be of any nationality;
• by the MSCA-PF deadline, the researcher must hold a doctoral degree or have successfully and unconditionally defended their doctoral thesis;
• researchers normally must have no more than eight years of full-time-equivalent research experience after the PhD, subject to deductions and extensions specified in the MSCA rules;
• applicants must comply with the mobility rule and normally must not have resided or carried out their main activity in Czechia for more than 12 months during the 36 months immediately preceding the MSCA deadline.

The standard duration of a European Postdoctoral Fellowship is 12–24 months.

Expression of Interest

Interested researchers should send their expression of interest to kincl@vse.cz by 24 August 2026.

Please provide:

1. CV, including your publication record and the exact date of PhD award or successful doctoral defence;
2. a one-page research concept outlining:

• the research problem you would like to address;
• the expected theoretical contribution;
• your proposed or preferred methodological approach;
• the empirical context you consider most suitable;
• how the idea fits the broader theme of human-AI judgement and cognitive augmentation;

1. a short statement of approximately 150–200 words explaining:

• what theoretical or methodological expertise you would bring to the project;
• how this expertise complements that of the supervisor and host;

1. details of your three research outputs most relevant to the proposed fellowship;
2. a brief confirmation that you believe you meet the MSCA-PF 2026 eligibility and mobility requirements.

Shortlisted candidates will be invited to an online meeting with the supervisor.

The selected candidate will then work closely with the supervisor and the Faculty of Management to develop a personalised MSCA Postdoctoral Fellowship proposal for submission to the 2026 call. The final project will be jointly developed and may differ substantially from the initial one-page concept.

Selection as the preferred candidate for proposal preparation does not constitute an employment offer. Employment at the host institution is conditional upon successful MSCA funding.