8th Workshop on Artificial Intelligence and Model-driven Engineering
Co-located with
MODELS.
Monday, October 5th 2026, Malaga, Spain.
Artificial Intelligence (AI) has become part of everyone's life. It is used by companies to exploit the information they collect to improve the products and/or services they offer and, wanted or unwanted, it is present in almost every device around us. Lately, AI is also impacting all aspects of the system and software development lifecycle, from their upfront specification to their design, testing, deployment and maintenance, with the main goal of helping engineers produce systems and software faster and with better quality while being able to handle ever more complex systems and software.
There is no doubt that MDE has been a means to tame until now part of this complexity. However, its adoption by industry still relies on their capacity to manage the underlying methodological changes, including among other things the adoption of new tools. To go one step further, we believe there is a clear need for AI-empowered MDE, which will push the limits of "classic" MDE and provide the right techniques to develop the next generation of highly complex model-based system and software systems engineers will have to design tomorrow.
This workshop provides a forum to discuss, study and explore the opportunities and challenges raised by the integration of AI and MDE.
We would like to address topics such as how to choose, evaluate and adapt AI techniques to Model-Driven Engineering as a way to improve current system and software modeling and generation processes in order to increase the benefits and reduce the costs of adopting MDE. We believe that AI artifacts will empower MDE tools and hence boost the advantages and adoption of MDE at the industry level.
At the same time, AI is software — and complex software, in fact. We therefore also believe that such an AI-powered MDE approach will benefit the design of AI artifacts themselves, especially when facing the challenge of designing trustworthy AI software.
Last but not least, although AI is the most popular branch of computer science to create and simulate intelligence, we also believe that any kind of technique that provides human cognitive capabilities and helps create intelligent software is within the scope of this workshop. Examples include knowledge representation techniques and ontologies that can be useful on their own or support other kinds of AI techniques.
This year, MDE Intelligence adopts the special theme “Assessing the Performance of MDE Intelligence Approaches”, inviting contributions that advance systematic, rigorous, and standardized evaluation practices for intelligent modeling systems.
Model-driven engineering (MDE) and artificial intelligence (AI) are two separate fields in computer science that can clearly benefit from cross-pollination and collaboration. There are at least two ways in which such integration — which we call MDE Intelligence — can manifest:
MDE can benefit from integrating AI concepts and ideas to increase its power: flexibility, user experience, quality, etc. For example, using model transformations through search-based approaches, or by increasing the ability to abstract from partially formed, manual sketches into fully-shaped and formally specified meta-models and editors.
AI is software, and as such, it can benefit from integrating concepts and ideas from MDE that have been proven to improve software development. For example, using domain-specific languages allows domain experts to directly express and manipulate their problems while providing an auditable conversion pipeline. Together this can improve trust in and safety of AI technologies. Similarly, MDE technologies can contribute to the goal of fair and explainable AI.
Topics of interest for the workshop include, but are not limited to:
Submissions must adhere to the ACM formatting instructions.
We ask for two types of contributions:
Page limits include references. Submissions must be uploaded through EasyChair.
All submissions will follow a single-blind review process where each paper will be reviewed by at least three members of the program committee. They will value the relevance and interest for discussions that will take place at the workshop. Accepted papers will be published in the joint workshop proceedings published by ACM.
Papers submitted to MDE Intelligence must not be under review or submitted for review elsewhere whilst under consideration for MDE Intelligence. Contravention of this concurrent submission policy, as stated explicitly by ACM here, will be deemed as a serious breach of scientific ethics, and appropriate action will be taken in all such cases.
Important update on ACM's new Open Access publishing model for
2026 ACM Conferences!
Starting January 1, 2026, ACM has fully transitioned to Open Access.
All ACM publications, including those from ACM-sponsored
conferences, will be 100% Open Access.
See the MODELS 2026 information on Open Access.
Note for this workshop: Extended abstracts will be covered by a
specific agreement with MODELS that will not require the payment of
any publication fee.
During the workshop, there will be a session of lightning talks around the topics that fall under the scope of the workshop.
We believe that lightning talks are a great opportunity for presenters to promote their work, to receive timely and helpful feedback, and to find new collaborations. At the same, these talks will be beneficial for the workshop participants as they may broaden their knowledge and they will be able to actively participate and engage in the discussion around the presented topics.
If you are interested in presenting a lightning talk, please follow the instructions below.
Presenters will have 2-3 minutes to communicate their ideas and they can choose to use up to one slide.
We encourage the submission of proposals around the following topics:
Proposals must be submitted via email to mdeintelligence2026@easychair.org. The deadline for submitting proposals is September 30th, 2026.
| 09:15 – 10:30 |
Session 1: Benchmarks and Evaluators for MDE Intelligence
Special Theme |
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| 10:30 – 11:00 | Break |
| 11:00 – 12:45 | Session 2: Generating Valid and Conformant Models |
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| 13:00 – 14:30 | Lunch Break |
| 14:30 – 15:45 |
Session 3: Modeling AI Systems and Processes
MDE for AI |
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| 15:45 – 16:15 | Break |
| 16:15 – 17:30 | Session 4: Agentic and Tool-Augmented Modeling |
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Juan de Lara, Universidad Autónoma de Madrid, Spain
Recent advances in generative artificial intelligence, especially large language models (LLMs), are reshaping software development. Coding assistants and, more recently, agentic programming systems such as Claude Code and Codex promise substantial productivity improvements and new opportunities for automation. As these tools gain traction, what role remains for model-driven engineering (MDE) in the current software development landscape?
In this talk, I argue that MDE has a valuable role to play in AI-assisted software development. Both approaches share an ambition: increase productivity by making code a by-product of a development process driven by higher-level specifications. AI-assisted development expresses intent through natural language, while MDE captures it via models. However, their approaches to automation differ drastically. LLM-based code generation is probabilistic, while traditional MDE relies on explicitly defined, deterministic transformations that can be complemented by formal verification to establish guarantees about the generated software.
The talk will explore how these approaches can be combined for mutual benefit. First, models can serve as intermediate representations: instead of generating code directly, AI agents can produce models that are validated and transformed into code using established MDE techniques. This combination brings together the flexibility of generative AI and the precision and control of MDE automation. Second, MDE principles and tools can strengthen emerging agentic practices such as spec-driven development, making specs more precise, analysable, and cognitive-effective. Together, these directions point towards a next generation of MDE, in which AI agents and model-driven techniques work together to bridge human intent and reliable software.
Juan de Lara is a Full Professor in the Department of Computer Science at the Universidad Autónoma de Madrid, Spain, where he co-leads the Modelling and Software Engineering Research Group (miso.es) with Esther Guerra. His research interests include automated software engineering, model-driven and low-code development, domain-specific languages and language engineering, conversational agents, and AI-assisted development. His work has resulted in numerous research tools, including Asymob, AToM3, metaDepth, merlin, and Gotten, and more than 270 publications in international journals and conferences. He has served as programme committee co-chair for MODELS, SLE, ICGT, ICMT, and FASE, and is a member of the editorial board of Software and Systems Modeling (SoSyM, Springer). He has also co-organised workshops on flexible modelling, multi-level modelling, and low-code development.
Dominik Bork
Boqi Chen
Lukas Netz
If you have questions, contact us by email at: mdeintelligence2026@easychair.org