New App Category: Mentoring

[marcotuts]

Abstract

We aim to define and streamline default course boundaries for AI integrations by introducing a standard course application category called "Mentoring." This category is designed to encompass a wide range of AI tools that support students within the course experience. The "Mentoring" category builds on the existing Discussions and Live app categories, offering a framework to showcase a growing library of integration options over time.

Detailed Product Proposal

https://openedx.atlassian.net/wiki/spaces/COMM/pages/4754014216/Proposal+Course+App+Category+-+Mentoring

Context & Background (in brief, if a Product Proposal is linked above)

By establishing standard API expectations, settings, and tools, this proposal seeks to increase visibility and accessibility of existing integrations for more Open edX instances through simplified configuration and directory options in the future.

For the learner experience, any Mentoring app category integration would be able to display interfaces in the learning MFE through various placements, including:

In-context sidebars, akin to existing discussion tools.

• Between content blocks within the course.

• As standalone pages, similar to Live and Discussions apps.

• Additional frontend plugin slots to be determined in the future.

For the educator experience, this proposal envisions that the Mentoring application category will:

• Surface existing mentoring tools, such as chatbots, AI sidebar tools, and other student support tools.

• Introduce standard and centralized mechanisms to provide per-content XBlock AI mentor prompt guidance and guardrails, ensuring custom pedagogical needs are met and understood by mentoring integrations.

More broadly, this proposal aspires to foster shared ecosystem development speed, enhancing AI course mentoring applications even as tools, models, and underlying technologies evolve rapidly.

Scope & Approach (in brief, if a Product Proposal is linked above)

Goals

• Enable seamless integration of mentoring tools via a standardized mechanism.
• Expand the scope of interactive and AI-driven learning within courses.
• Enhance user experience for both course creators and learners through easy-to-use mentoring integrations.
• Foster engagement by providing diverse, personalized, and adaptive mentoring options.

Value & Impact (in brief, if a Product Proposal is linked above)

Use Cases

1. AI Chatbot Mentoring: A course integrates a chatbot to provide students with real-time, context-sensitive guidance on assignments and concepts.
2. Peer Mentorship Tools: Enable students to connect and collaborate with peers in a guided format, facilitated by structured AI prompts.
3. Personalized Feedback Mechanisms: Allow instructors to deploy AI tools that deliver detailed, personalized feedback to learners.
4. Career Coaching Integration: Courses with professional development goals could use third-party AI tools for career advice and interview preparation.
5. Language Practice Conversations: Integrating conversational AI to help learners practice language skills within relevant course contexts.

Milestones and/or Epics

Implementation

• Phase 1: Mentoring App Category Basics
• Phase 2: App Category Customization & Safety / Privacy Guardrails
• Phase 3: Demo Mentoring App / Starter Tools
• Phase 4: Expanded Mentoring App Integration List & Iteration

Named Release

TBD

Timeline (in brief, if a Product Proposal is linked above)

TBD based on proposal feedback and input

Proposed By

Schema Education

Additional Info

No response

[github-actions]

Thanks for your submission, @openedx/openedx-product-managers will review shortly.

[jmakowski1123]

Tap community folks who have build similar tools as starting point

[pdpinch]

While I can definitely see the value of having a standardized, API-based approach to this type of service, one thing to keep in mind is instructor oversight and agency. Scenarios I can imagine are

1. courseware based: for example, problems that ask for reflection or creativity -- or a simple survey -- where it would be distracting to offer students an AI mentor.

2. time-based: for example, midterms or, especially, proctored exams, when the platform shouldn't be offering students AI mentors.