TrUE AI – Trust and User experience evaluation in AI applications

Images: ChatGPT4, pexels.com; Music: SamsungStudio

TRUE-AI helps companies find out how well their AI systems are received by users. Through observations, interviews and surveys, the company assesses whether the system is user-friendly and whether staff trust it. This leads to specific recommendations for practical improvements..

Development status

Development and implementation

Scientific basis

Development and implementation

Initial field trials

Evaluation and optimisation

Ready for practical use

What problem do many companies face?

In many companies, there is a lack of acceptance of AI that has either already been introduced or is planned for introduction. This lack of acceptance means that AI cannot be utilised to its full potential. There are many reasons for this: on the one hand, there may be trust issues, which manifest themselves as a lack of understanding or a general aversion to AI. On the other hand, there may be user experience issues if the system is complicated or inefficient to use, or difficult to learn. Both aspects – user experience and trust – are essential for the acceptance and optimal use of AI.

How does the tool help to solve the problem?

TRUE-AI enables a systematic assessment of the user experience and trust in AI. This involves not only carrying out assessments, but also identifying the underlying reasons for these assessments. On this basis, specific opportunities for optimisation can then be identified.

The tool is applied in three phases:

  1. Think-Aloud: Users carry out typical tasks using the AI whilst verbalising their thoughts, actions, feelings and emotions
  2. Interviews: In-depth exploration of observations through targeted questions on behavioural patterns, perceptions, attitudes and intentions
  3. Questionnaire: Verification of findings and improving representativeness

The phases can be applied either sequentially or in isolation.

How is the tool used within the company?

3 stages in which the tool is used: think-aloud, interviews, questionnaires.

What is the (time) cost to the company associated with this objection?

The process is carried out in three phases that build on one another. The total time required depends on the specific AI system and the business context. The phases can be carried out sequentially or individually:

Phase I (Think-Aloud):

  • Each session lasts approximately 60–90 minutes
  • Sessions for 1–5 participants
  • Total time required, including analysis of all the data collected: approximately 10–15 hours

Phase II (Interviews):

  • Approximately 60–90 minutes per interview
  • Sessions for 5–15 participants
  • AI transcription and checking: approximately 30–45 minutes per interview
  • Total time required, including analysis of all the data collected: approximately 40–60 hours

Phase III (Quantitative Survey):

  • 80 or more participants
  • Creating an online questionnaire: approximately 3–4 hours
  • Can be carried out in parallel with other phases
  • Statistical analysis: approximately 8–12 hours
  • Total time required, including analysis of all the data collected: approximately 20–30 hours

What added value does the tool bring to the company?

With TRUE-AI, companies can:

  • Systematically identify specific challenges relating to trust and the user experience of your AI system
  • Derive specific measures for the further development of the system from this
  • Carry out a human-centred optimisation of the AI system
  • Increase user acceptance of the system
  • Ensuring the AI system is used to its full potential in day-to-day business operations

This enables companies to make the most of their investments in AI systems and realise their full potential.

Scientific publications

Bunde, E., Eisenhardt, D., Sonntag, D., Profitlich, H. J. and Meske, C. (2023). Giving DIAnA More TIME – Guidance for the Design of XAI-Based Medical Decision Support Systems. International Conference on Design Science Research in Information Systems and Technology (DESRIST), 2023, pp. 107-122.

The tool was evaluated in part using the AI system DIAnA (Dermatological Images – Analysis and Archiving) as a case study, a web-based system developed by DFKI for AI-assisted skin lesion classification. The evaluation was carried out using think-aloud protocols and interviews with doctors, trainee doctors and patients, from which design requirements and design principles were derived. The principles developed were subsequently evaluated positively by software developers and UI/UX designers.

Contact persons
Erdi Ünal
Erdi Ünal
Institute for Work Science