Skip to Main Content

Open Educational Resources Collective Publishing Workflow

Develop and Create Content with AI

The Development of AI Tools

Artificial intelligence (AI) has evolved rapidly in recent years, particularly with the emergence of large language models (LLMs) and generative AI tools (GenAI). What began as experimental technology has become increasingly accessible, with tools now available to assist with writing, research, analysis and creative tasks. These developments have opened new possibilities for educational content creation and scholarly work.

AI tools can process large amounts of information quickly, generate text and images, identify patterns and assist with routine tasks. For authors and educators, this technology offers potential support for various aspects of content development – from brainstorming ideas to editing drafts to creating supplementary materials.

Important Principles for using AI in OER

  • AI is a tool to assist, not replace, human expertise and judgment.
  • Authors remain responsible for accuracy, quality and scholarly integrity.
  • All AI-generated content must be reviewed, verified and adapted to your context.
  • Consider copyright and licensing implications when using AI tools.
  • Be transparent about AI use where appropriate.
  • Ensure accessibility and inclusion standards are maintained.

Remember, AI is an evolving field and its applications in OER continue to change. You should remain informed about current developments, institutional policies, and relevant guidelines to ensure compliance with best practices and ethical standards. 

Opportunities and Considerations

While AI presents genuine opportunities to streamline certain aspects of OER publishing, it's important to approach these tools thoughtfully. AI has notable limitations: it can produce inaccurate information, reproduce biases present in its training data, generates content that infringes copyright and lacks the nuanced understanding that human expertise provides. The technology works best as a collaborative tool that augments human capabilities rather than replacing human judgment and intellectual effort.

As we integrate AI into educational resource development, we must remain mindful of questions around academic integrity, equity of access, environmental impact, data privacy and the appropriate role of automation in scholarly work. Different institutions and individuals will have varying perspectives on where and how AI should be used – and that's appropriate. The goal is to make informed choices that align with your values and project needs.

Acknowledging AI Use and Ethical Considerations

When using AI in OER creation it is important to acknowledge its role clearly and openly. This includes stating which AI tools were used, what tasks they supported, the extent of human oversight and any prompts or parameters used for AI-generated text or visuals. Acknowledging these details supports transparency, helps others understand how the resource was produced and aligns with good OER practice.

These elements can be brought together in a diligence statement that summarises how AI contributed to the work and where human decisions shaped the final outcome. The ‘GenAI for Legal Practice’ OER includes a useful example of a diligence statement in its acknowledgements.

Ethical considerations should also be recognised. AI systems consume significant computational resources, which contributes to environmental impact, and many models are trained on large datasets that may contain copyrighted or culturally sensitive material. When integrating AI into OER workflows, weigh these considerations and aim to minimise unnecessary processing. Transparency, accountability and environmental responsibility should underpin all AI-supported activities.

AI in OER Publishing

The following sections provide practical suggestions for how AI tools might support authors and library staff throughout the OER publishing process, while emphasising the critical importance of human oversight and expertise.

The suggestions are organised according to the seven stages of the CAUL OER Collective Publishing Workflow. Whether you choose to use AI extensively, minimally or not at all in your OER project, understanding its capabilities and limitations will help you make informed decisions about your publishing workflow.

Stage 1: Initiate

Where AI can help in this stage:

 
Understanding OER Concepts
  • Ask AI to explain OER terminology and concepts in plain language
  • Generate summaries of key OER principles for team discussions
  • Create comparison tables between traditional and OER publishing
Exploring Possibilities
  • Brainstorm potential projects based on needs
  • Generate questions to ask yourself before starting an OER project
  • Identify gaps in existing OER for your purpose
Example Prompts
  • "Explain Creative Commons licenses in simple terms for academic authors"
  • "What questions should I consider before starting an open textbook project?"
  • "Help me brainstorm OER projects suitable for [discipline/course]"
Cautions
  • Verify all AI-provided information about licensing and copyright
  • Don't rely solely on AI for understanding legal requirements

Stage 2: Plan

Where AI can help in this stage:

 
Project Planning
  • Generate project timelines based on your parameters
  • Create task checklists for different team members
  • Draft initial project proposals or expressions of interest
  • Suggest potential co-authors or contributors
Copyright Planning
  • Explain copyright concepts in different jurisdictions
  • Generate templates for tracking copyright permissions
  • Create attribution statement templates
Team Organisation
  • Draft role descriptions for writing and publishing teams
  • Generate agreement templates for co-authors
  • Create communication guidelines for collaborative teams
Example Prompts
  • "Create a 12-month timeline for developing an open textbook with the following milestones: [list]"
  • "Draft a role description for a copyeditor working on an OER project"
  • "Generate a template for tracking third-party content and attributions"
Cautions
  • Verify all legal and copyright information with appropriate experts
  • Customise templates to your institutional context
  • Don't use AI-generated contracts without legal review

Stage 3: Draft

Where AI can help in this stage:

 
Content Development
  • Generate initial content outlines based on learning objectives
  • Suggest chapter structures and pedagogical elements
  • Create drafts of textbook elements (i.e., glossaries, summaries)
  • Generate discussion questions or activities
  • Overcome writer's block by suggesting different approaches
Writing Support
  • Improve clarity and readability of draft text
  • Suggest alternative phrasings for complex concepts
  • Check consistency of terminology across chapters
Inclusive Language
  • Review text for potentially biased or non-inclusive language
  • Suggest alternatives to problematic terms
  • Help identify gaps in representation or perspective
Example Prompts
  • "Create an outline for a textbook chapter about [topic] with these learning objectives: [list]"
  • "Generate 10 discussion questions for a chapter about [topic] suitable for undergraduate students"
  • "Review this paragraph for inclusive language and suggest improvements: [paste text]"
  • "Suggest examples from diverse cultural contexts to illustrate [concept]"
Cautions
  • AI-generated content is a starting point, not a final product
  • Always fact-check AI-generated information against reliable sources
  • Ensure examples are culturally appropriate and accurate (AI may generate stereotypes or inaccuracies)
  • Never use AI-generated content without substantial revision and verification
  • Be aware that AI may reproduce copyrighted material–always check and rewrite

Stage 4: Design

Where AI can help in this stage:

 
Accessibility Planning
  • Generate alt text descriptions for images (as a starting point)
  • Suggest ways to make complex tables more accessible
  • Review text for reading level and complexity
  • Create plain language summaries of technical content
Style Consistency
  • Check adherence to your chosen style guide
  • Identify inconsistencies in formatting or terminology
  • Generate style guide summaries for team reference
Visual Elements
  • Suggest ideas for diagrams or infographics to illustrate concepts
  • Create image generation prompts for custom illustrations
  • Help describe complex visuals in accessible language
Example Prompts
  • "Suggest alt text for an image showing [description]"
  • "Simplify this paragraph to a reading level appropriate for first-year undergraduates: [paste text]"
  • "Review this text for consistency with APA style guidelines: [paste text]"
Cautions
  • AI-generated alt text must be reviewed for accuracy and appropriateness
  • Don't rely on AI alone for accessibility compliance–use proper accessibility checking tools
  • AI-generated images may have copyright implications

Stage 5: Review

Where AI can help in this stage:

 
Editing Support
  • Identify grammatical errors and typos
  • Suggest improvements to sentence structure and flow
  • Check for consistency in voice and tone
  • Generate proofreading checklists
Content Review
  • Summarise long chapters for review purposes
  • Identify potential gaps or missing topics
  • Suggest areas needing further work
  • Compare your content against learning objectives
Peer Review Facilitation
  • Generate peer review guidelines and questions
  • Create review templates aligned with your criteria
  • Summarise reviewer feedback into themes
Example Prompts
  • "Identify any inconsistencies in terminology used across these three chapters: [paste excerpts]"
  • "Create a peer review rubric for evaluating an open textbook chapter on [topic]"
  • "Summarise this reviewer feedback and identify key themes: [paste feedback]"
Cautions
  • AI cannot replace expert peer review
  • Don't share unpublished content with AI tools that store or learn from inputs
  • Technical and factual accuracy must be verified by subject experts

Stage 6: Publish

Where AI can help in this stage:

 
Metadata and Descriptions
  • Generate book descriptions for catalogues and repositories
  • Create keywords and subject tags
  • Draft promotional summaries in various lengths
  • Write social media announcements
Documentation
  • Create user guides for adopters
  • Generate FAQs
  • Create instructor notes or teaching guides
Accessibility Checks
  • Generate transcripts for audio/video content (as a starting point)
  • Review final text for accessibility issues
  • Create accessible summaries of complex sections
Example Prompts
  • "Write a 150-word description of this textbook for a library catalogue: [provide overview]"
  • "Generate 10 keywords for an open textbook about [topic]"
  • "Create an FAQ for instructors considering adopting this textbook on [topic]"
  • "Draft a social media announcement for the publication of this OER: [provide details]"
Cautions
  • Review all metadata for accuracy before publishing
  • Ensure promotional content accurately represents your work
  • AI-generated transcripts require human verification

Stage 7: Evaluate

Where AI can help in this stage:

 
Feedback, Collection and Analysis
  • Generate user feedback surveys for students and instructors
  • Create evaluation frameworks for measuring OER impact
  • Analyse survey responses and identify themes
  • Summarise large volumes of qualitative feedback
Usage Analytics
  • Suggest metrics for tracking OER adoption and use
  • Create templates for documenting usage data
  • Help interpret analytics data and identify trends
  • Generate visualisation suggestions for impact reports
Impact Assessment
  • Draft impact reports based on collected data
  • Create comparison frameworks (e.g., cost savings, outcomes)
  • Generate case studies from evaluation data
  • Suggest improvements based on feedback analysis
Promotional Materials
  • Generate promotional text highlighting evaluation results
  • Create social media content about OER impact
  • Draft presentations about your OER's success
  • Write blogs or articles about the project outcomes
Example Prompts
  • "Generate survey questions to gather feedback from students using this textbook about accessibility, clarity and usefulness"
  • "Analyse this feedback data and identify the top 5 themes: [paste data]"
  • "Create a template for tracking adoption statistics for an open textbook"
  • "Draft a brief impact report based on these usage statistics: [provide data]"
  • "Suggest ways to visualise cost savings data for students using this OER"
Cautions
  • Review survey questions for bias and leading language
  • Don't rely on AI to interpret qualitative feedback – human judgement is essential
  • Verify any statistical interpretations or calculations
  • Ensure promotional content about impact is accurate and not overstated
  • Be mindful of privacy when analysing student feedback

Further Resources

Want to learn more? Explore the UNESCO Generative AI for OER Course on OER Commons to understand how AI can enhance OER.

References

Adams, M. (2025). GenAI for legal practice. Swinburne University of Technology. https://oercollective.caul.edu.au/gen-ai-legal-practice