Open edX AI Extensions Working Group Charter

Last updated: 2025-08-08
Status: Draft
Point of contact: @Felipe Montoya and @Santiago Suarez
Slack Channel: #wg-ai-extensions
Meeting Cadence: Monthly, TBD

AI Working Group – Designated Representatives by Organization

To ensure consistent participation and active contribution, each organization involved in the AI Working Group is asked to designate an official representative. This person commits to attending meetings regularly and participating actively in discussions and initiatives. The table below keeps track of designated representatives from each organization.

Purpose

The AI Extensions Working Group exists to explore, guide, and support the adoption and integration of artificial intelligence capabilities into the Open edX platform. We aim to do this in a way that is ethical, pedagogically sound, privacy-aware, and technically sustainable—benefiting learners, educators, course authors, and platform operators.

This working group serves as a collaborative forum for community members, core contributors, partner organizations, and edtech researchers to identify promising use cases, surface requirements, and align on shared development efforts.

Subgroups

To focus our efforts and accommodate different areas of expertise, the working group includes two complementary subgroups:

• Platform & Product Boundaries Subgroup: Focused on defining platform-level capabilities, APIs, extension points, and infrastructure considerations to enable AI tools and features across Open edX.

• Educator & Authoring Subgroup: Focused on supporting instructional use cases such as AI-assisted course creation, learner feedback, grading, and tutoring; this group collaborates closely with course authors, instructional designers, and faculty.

These subgroups may hold separate sessions or working sprints but maintain shared visibility through the main working group meetings and shared documentation.

Coordination & Affiliation

The working group is guided by a Chair / Co-Chair model to ensure operational health, continuity, and goal alignment. Chairs are responsible for facilitating meetings, coordinating subgroup efforts, driving key initiatives forward, and representing the group in Open edX governance discussions.

In addition, platform providers, institutions, and Open edX contributors are invited to formally affiliate themselves with the working group. These affiliations recognize active participants, encourage collaboration across organizational boundaries, and provide transparency on who is contributing to AI-related efforts across the ecosystem.

A publicly listed section of affiliated contributors and organizations will be maintained on the community wiki.

Scope of Work

The working group will:

• Identify and prioritize impactful use cases for AI in Open edX (e.g. feedback generation, personalized learning, authoring assistance, learner support).

• Propose architectural patterns, API designs, and extension points to enable AI features across the platform.

• Maintain awareness of emerging AI technologies (LLMs, speech recognition, computer vision, etc.) relevant to online learning.

• Create or contribute to AI-focused development efforts, pilots, and proof-of-concepts.

• Develop community guidance on responsible AI use in the context of Open edX (e.g. transparency, bias mitigation, data handling).

• Facilitate knowledge sharing and collaboration across product, engineering, and instructional design stakeholders.

Actionable Goals

To ensure momentum and clarity, the group will define and track quarterly objectives. These may include:

• Publishing draft architecture for AI integration points

• Launching pilots or proof-of-concept tools

• Hosting community showcases or technical deep-dives

• Soliciting feedback from educators on usability of new tools

A running list of current objectives and milestones will be maintained on the working group’s wiki page.

Out of Scope (for now)

• Research and development of proprietary AI models not intended for community sharing.

• Deep infrastructure work related to GPU hosting or enterprise-level MLOps (though we welcome integration perspectives from platform providers).

• Non-AI automation (e.g. simple scripting or rule-based logic unrelated to ML/AI).

Membership

Membership is open to any contributor or partner in the Open edX community with interest or expertise in:

• Artificial Intelligence / Machine Learning

• Learning Experience Design

• Platform Architecture

• Educational Data & Analytics

• Product Design and Strategy

We encourage participation from both technical and non-technical contributors.

Deliverables

The working group will aim to produce:

• A set of prioritized AI use cases aligned with Open edX goals.

• Technical recommendations (e.g. extension frameworks, service contracts, APIs).

• Reference implementations or prototypes of high-value AI features.

• Community documentation and ethical AI usage guidelines.

• Integration guidance for common AI tooling (e.g. OpenAI, Hugging Face, LangChain, Whisper, etc.).

Ways of Working

• Meetings: Held monthly with rotating facilitators. Agenda and notes posted in the Meeting Notes.

• Communication: Asynchronous collaboration on Slack (#wg-ai-extensions) and via Discourse as needed.

• Documentation: Hosted in the Open edX community wiki under a dedicated AI Extensions section.

• Decision Making: Consensus-driven, with escalation to the Product Working Group or TOC for major proposals.

• Feedback: This charter and group process will be reviewed semi-annually. Members are encouraged to suggest revisions via the wiki or Slack.

Relationship to Other Groups

This working group reports progress to and coordinates with the Product Working Group. It may collaborate with:

• UX & Learner Experience Groups on user-centered design

• Frontend/Backend Working Groups on implementation strategies

• Data Working Group on usage analytics and model integration

• Educator and Course Author Groups on piloting AI authoring tools

Initial Focus Areas (2025)

• AI-assisted course authoring (e.g., problem generation, feedback scaffolding)

• Personalized learner support (e.g., tutor bots, knowledge checks)

• Content summarization and translation

• Ethical guidance and opt-in practices for AI use

• Infrastructure proposals for plugin-based AI service integration