DOI: 10.5281/zenodo.17962100
title: Data from: Toward AI-Augmented Engineering Workflows: Insights from the European Rover Challenge

description: Abstract

The increasing adoption of Artificial Intelligence (AI) tools offers new opportunities to support engineering design and coordination, yet their most effective applications in complex workflows remain unclear. To investigate this, we collected survey data from teams participating in the European Rover Challenge (ERC) [1], an international robotics competition in which student teams design, build, and integrate complex planetary rover systems under strict technical, regulatory, and time constraints. This dataset comprises responses to a 40-question survey distributed to all 25 ERC 2025 teams, yielding 104 responses from 14 teams. The data capture team organization, development practices, AI usage, and challenges in rover design and integration. The analysis of these responses is presented by Sadik et al. [2].

 

Dataset organization

The dataset consists of a single MS Excel file. Each row corresponds to the responses of an individual team member. Columns include anonymized team names, the role of the respondent within the team, and answers to the survey questions listed below.

The survey was designed to capture perspectives from different roles within ERC teams using a role-dependent branching structure. Participants identified themselves as team leaders, group leaders, or team members and were directed to role-specific sets of questions. Team leaders responded to questions concerning team organization, experience, and knowledge transfer; group leaders focused on task assignment and coordination; and team members addressed daily execution and workflow-related practices. Following these role-specific sections, all respondents completed a shared block of questions covering core aspects of the development process, including task definition, dependencies, scheduling, integration activities, communication practices, and the use of AI tools.

Although the survey comprised 40 core questions, several were instantiated in multiple contextual forms. Consequently, the dataset includes more columns, and the list below contains more items than the original number of survey questions.

 

Response items



Select the name of your team (2025):

Select your role in the team:

What was your team size (number of active members)?

For how many years did your university participate in ERC/URC?

How was the team organization structure? 

Are you using standard processes for your processes, which one and for which part?

Do you plan ahead for knowledge transfer, and how do you make sure you gather all information needed for the future transfer?

What share of the team 2024 was kept in the challenge 2025?

Rate the knowledge transfer process:

What are the biggest challenges in knowledge transfer?

Which knowledge transfer methods were used? 

How do you usually assign tasks to your group members?

What are the main problems you experienced in assigning tasks?

How do you monitor whether tasks are progressing on time?

How do you decide when a task is “completed”?

What are the main problems you experienced in monitoring tasks?

How was the team organization structure?

How would you rank the effectiveness of the organization structure for your work?

How were tasks assigned to you?

Rate the level of quality of the following aspects: [Knowledge transfer process]

Rate the level of quality of the following aspects: [Knowledge transfer content]

Rate the level of quality of the following aspects: [Task definition]

Rate the level of quality of the following aspects: [Technical constraints definition]

Rate the level of quality of the following aspects: [Deadline and duration]

Rate the level of quality of the following aspects: [Dependencies on other tasks]

Rate the level of quality of the following aspects: [Acceptance/done criteria]

Rate the level of quality of the following aspects: [Time to design]

Rate the level of quality of the following aspects: [Time to manufacture]

Rate the level of quality of the following aspects: [Time to perform integration]

Rate the level of quality of the following aspects: [Time to perform test]

For how many years do you personally work at ADC?

Where are you involved?

Estimate time spent in different phases: [Recruitment & Onboarding]

Estimate time spent in different phases: [Knowledge Transfer]

Estimate time spent in different phases: [Background Research]

Estimate time spent in different phases: [Requirements & Concept (Pre-selection)]

Estimate time spent in different phases: [Requirement & Concept (Post-selection)]

Estimate time spent in different phases: [Detailed Design]

Estimate time spent in different phases: [Development]

Estimate time spent in different phases: [Testing & Validation]

Estimate time spent in different phases: [Final Testing before Final Event]

Estimate time spent in different phases: [Documentation]

Estimate time spent in different phases: [Sponsor Search (Marketing)]

Estimate time spent in different phases: [Public Relation]

A posteriori, do you think any of these tasks could be made more efficiently? [Recruitment & Onboarding]

A posteriori, do you think any of these tasks could be made more efficiently? [Knowledge Transfer]

A posteriori, do you think any of these tasks could be made more efficiently? [Background Research]

A posteriori, do you think any of these tasks could be made more efficiently? [Requirements & Concept (Pre-selection)]

A posteriori, do you think any of these tasks could have been made more efficiently? [Requirement & Concept (Post-selection)]

A posteriori, do you think any of these tasks could be made more efficiently? [Detailed Design]

A posteriori, do you think any of these tasks could be made more efficiently? [Development]

A posteriori, do you think any of these tasks could be made more efficiently? [Testing & Validation]

A posteriori, do you think any of these tasks could have been made more efficiently? [Final Testing before Final Event]

A posteriori, do you think any of these tasks could be made more efficiently? [Documentation]

A posteriori, do you think any of these tasks could have been made more efficiently? [Sponsor Search (Marketing)]

A posteriori, do you think any of these tasks could have been made more efficiently? [Public Relation]

Percentage of work requiring rework/revision in term of time: [Concept & Requirements]

Percentage of work requiring rework/revision in term of time: [Design]

Percentage of work requiring rework/revision in term of time: [Development]

Percentage of work requiring rework/revision in term of time: [Integration]

Percentage of work requiring rework/revision in term of time: [Testing]

Percentage of work requiring rework/revision in term of time: [Documentation]

Which were the most recurring loops that result in wasted effort? 

What caused the biggest delays? 

How do you track requirements and with which tools? 

Do you use AI Tools (ChatGPT, Claude,...) in your development process?

Which AI tool and for which purpose?

How is communication organized?

How often does communication take place?

Which tools did you use to communicate/coordinate? 

Which communication problems affected you most often? 

What was the biggest cause of conflicts? 

What was the most impacting issue that affected the quality of the final product?

What would you change in your team’s strategy?

If an AI assistant could solve one organizational problem for you, what should it do?

If an AI assistant could solve one technical problem for you, what should it do?


 

Data anonymization statement

This dataset contains anonymized survey responses from student engineering teams. No personal identifiers (names, emails, institutions) are included. Free-text responses were reviewed and generalized to prevent indirect identification (e.g., geographic references).

 

Acknowledgements

The authors gratefully acknowledge the organizers of the ERC, especially the European Space Foundation under the leadership of Łukasz Wilczyński, for their support and for facilitating access to the participating teams. We also wish to express our sincere thanks to the members, group leads, and team leads of the ERC 2025 teams who generously shared their time and perspectives by completing the survey: 4Space (Spain), AAU Space Robotics (Denmark), AGH Space Systems (Poland), ASU ROAR (Egypt), DJS Antariksh (India), FHNW Rover Team (Switzerland), FRoST (Germany), KNR Rover Team (Poland), Mars Rover Manipal (India), Orion Team (Poland), OzU Rover Team (Turkey), ProjectRED (Italy), Sapienza Technology Team (Italy), UPC Space Program (Spain), WARRYZN Space Robotics (Germany). This work would not have been possible without their openness in sharing their experiences and challenges.

 

References

[1] European Rover Challenge (ERC) – official website. https://roverchallenge.eu/, accessed: 2025-02-19

[2] Sadik, A.R., Joublin, F., Bujny, M., Ceravola, A., Smith, J.: Toward AI-augmented Engineering Workflows: Insights from the European Rover Challenge.