AI systems are advancing rapidly, but their adoption and impact depend on more than technical capabilities. It requires balancing algorithmic performance with trust-building, encouraging computational thinking skills, and understanding the responses of users who interact with such systems.
Trust is central to AI development and deployment. Users need to believe that AI systems are reliable, safe, and aligned with human values. This includes trust in accuracy (does the AI give correct answers?), transparency (can its decisions be explained?), and fairness (does it avoid bias?). Without trust, even highly capable AI systems may face resistance or limited adoption. Trust may depend on knowledge of the domain and of AI. Domain experts do not necessarily have knowledge of AI.
The Novice/Expert problem refers to the paradox that evaluation of the answers of an AI system requires domain expertise, but the lack there off is the motive for using the AI system. AI systems lead to a four new situation in which users may be characterized as novices or experts both on the knowledge of the domain and on knowledge of AI. Computational thinking is an educational concept that emphasizes teaching and assessing computer science–related ways of thinking across all levels of schooling. These ways of thinking—including problem decomposition, abstraction, and algorithmic reasoning—are also relevant for understanding, designing, and interacting with AI system. It impacts the way engineers build models and helps users better understand how to interpret outputs and evaluate system behavior. It is increasingly seen as a core skill for working alongside AI.
A number of factors influence how people perceive and interact with AI. These include cognitive biases (such as over-trusting automated outputs or fearing machine decisions), emotional responses (such as anxiety about job displacement or excitement about productivity gains), and social perceptions (such as viewing AI as authoritative or human-like). These factors affect how much users rely on AI, how they judge its credibility, and whether they accept or reject its recommendations.
The main objective of this thesis is to examine how human and cognitive factors influence the development, adoption, and effective use of modern AI systems, under the three dimensions. It aims to explore the relationship between technical design principles and human perception in shaping AI interaction.
More specifically, the thesis seeks to:
Météo-France forecasters and students of the National School of Meteorology (associated to Météo-France) with different levels of expertise will constitute the population for this study. The advantage is that one can follow them at several stages of the process and therefore evaluate them at the beginning of their learning, then consider several phases of awareness of AI (potentially different between groups of users).
Qualification: Master or equivalent in computer science.
Researched skills:
Doctoral school: MSTII, Université Grenoble Alpes.
Advisor: The thesis will be advised by Cássia Trojahn dos Santos – LIG (Cassia:Trojahn-dos-Santos#univ-grenoble-alpes.fr) and Erica De Vries – LaRAc (erica:devries#univ-grenoble-alpes:fr).
Group: The work will be carried out in the mOeX team common to INRIA & LIG in cooperation with the LaRac laboratory. mOeX is dedicated to study knowledge evolution through adaptation. It gathers researchers which have taken an active part these past 15 years in the development of the semantic web and more specifically ontology matching and data interlinking.
Place of work: The position is located at INRIA Montbonnot (near Grenoble) a main computer science research lab, in a stimulating research environment.
Hiring date: October 2026.
Funding and employer: The project is funded by the MIAI cluster. The employer will be INRIA; the candidate will be subject to ZRR clearance.
Duration: 36 months
Deadline: as soon as possible.
Contact: For further information, contact us.
File: Provide Vitæ, motivation letter and references. It is very good if you can provide a Master report and we will ask for your marks in Master, so if you have them, you can join them.