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Benefits of Using AI for OERs
Benefits of Using AI for OERs
AI offers significant advantages for instructors and instructional designers working with OERs. For instructors, AI tools streamline content creation, producing high-quality images, text, audio, and videos quickly and cost-effectively. This efficiency frees up valuable time, allowing educators to focus more on teaching and engaging with students. Additionally, AI can adapt materials to meet diverse student needs, enhancing personalized learning experiences. For instructional designers, AI goes a step further by providing data-driven insights into how students interact with materials. This helps designers refine and improve resources, ensuring they are as effective as possible. The ability to rapidly prototype and test new content ideas with AI also accelerates the design process, fostering innovation in educational resources.
Challenges and Considerations
Challenges and Considerations
While generative AI tools offer significant value, there are numerous ethical concerns and potential risks to consider when using these tools to develop OER. Understanding these challenges is crucial to making informed decisions and ensuring responsible use of AI for OER creation.
Attribution
Attribution
The content of “Challenges and Considerations” was adapted from the following works:
Generative artificial intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications. Michal Bobula. Journal of Learning Development in Higher Education, (30). Copyright (c) 2024 Journal of Learning Development in Higher Education. Licensed under CC BY 4.0.
"Getting Started: OER Publishing at BCcampus" by the BCcampus OER Production Team and is licensed under CC BY 4.0.
"Hand holds a piece of paper with text fake over fact text. Fake or Fact concepts" by wuestenigel is licensed under CC BY 2.0.
- Inacurracy
Generative AI systems can sometimes produce inaccurate or fabricated responses, a phenomenon known as hallucinations. Their advanced content generation capabilities can create a false sense of reliability, leading users to blindly trust their output. For instance, when asked for literature references, generative AI can invent convincing but non-existent titles and authors.
2. Bias
2. Bias
Generative AI tools do not operate based on ethical principles and cannot distinguish between right and wrong, true and false. They simply gather data from the databases and texts they process online. Therefore, they may inherit the cognitive biases in their training data and produce biased and discriminatory responses. As a result, generative AI can perpetuate and even intensify existing biases and unfairness in society, such as gender stereotypes and other forms of discrimination.
3. Information Privacy
3. Information Privacy
Generative AI systems process extensive amounts of data, increasing the risk of cyberattacks that could result in unauthorized access or misuse of sensitive information. Moreover, the lack of transparency from developers about how they store and use data collected during user interactions raises significant concerns about information privacy and security.
4. Intellectual Property and Copyright
4. Intellectual Property and Copyright
Copyright and intellectual property concerns arise in two main areas: the content used to train the AI tool and the content generated by the AI tool.
Training AI with copyrighted materials: Many generative AI tools are trained on copyrighted works, often without the permission of copyright holders. Lawsuits have been filed against companies developing these tools, with creators arguing that AI tools create unauthorized derivatives. The legal outcomes depend on whether courts view this use as 'fair use' or unauthorized copying. This means AI-generated content could potentially be subject to copyright claims.
Copyrightability of AI-generated works: Under the current copyright legislation in the United States, copyright protection requires human authorship. Therefore, AI-generated outputs alone cannot be protected by copyright and are considered public domain. However, if a human selects, arranges, or modifies AI output in a sufficiently creative way, the resulting work might be eligible for copyright. In such cases, the copyright will only protect the human-authored aspects of the work, not the AI-generated portions (U.S. Copyright Office, 2023).
Relevant Resources
Relevant Resources
- For general information on Fair Use, refer to this section.
- To learn more about copyright and Fair Use in the context of training AI, refer to this article in the Houston Law Review.
- To stay informed with the latest AI and copyright policy updates, visit the U.S. Copyright Office’s dedicated AI Initiative website.
5. Sustainability
5. Sustainability
Generative AI requires massive amounts of electricity to function, raising concerns about its environmental sustainability. The significant energy consumption of these systems has prompted examinations and debates about their long-term impact on the environment and the need for more energy-efficient solutions in AI development and deployment.
Guidelines and Recommendations
Guidelines and Recommendations
Using AI comes with its challenges and risks, so it is important to exercise caution when utilizing AI-generated content. This section offers some guidelines to consider if you plan to use generative AI tools during the OER content creation process. While we provide these suggestions, keep in mind that AI technologies and practices are rapidly developing, and these recommendations may change as the field and legislation evolve.
Attribution
Attribution
The content of “Guidelines and Recommendations" was adapted from the following works:
"Getting Started: OER Publishing at BCcampus" by the BCcampus OER Production Team is licensed under CC BY 4.0.
"Creating OER with AI" by TCC Libraries is licensed under CC BY 4.0.
"Guidelines for Using Generative AI Tools in Open Educational Resources" by Affordable Learning Georgia is licensed under CC BY 4.0.
- Verify Output Accuracy
As an OER author, you are ultimately accountable for the content you share. Therefore, you must manually verify the accuracy of any AI-generated content. Avoid using AI-generated content if you do not have the expertise to confirm its accuracy. Additionally, it is good practice to have AI-generated content reviewed by multiple subject matter experts to ensure its validity and accuracy.
2. Assess Output for Bias
2. Assess Output for Bias
Carefully examine AI-generated content for bias, including language or images that reinforce cultural or societal stereotypes around race, ethnicity, color, ancestry, place of origin, political beliefs, religion, marital status, family status, ability, sex, gender identity and expression, sexual orientation, age, and class and/or socioeconomic status.
3. Document and Disclose AI Use
3. Document and Disclose AI Use
Maintaining detailed documentation is crucial when reusing openly licensed material; the same applies to AI-generated content. When using a generative AI tool during the OER content creation process, keep track of the following information*:
Location: Specify where the AI-generated content is used in the OER.
Content Generated: Record what content was created by AI.
Tool Used: Document the tool used, including the version used and the link to the tool.
Usage Details: Note how you used the tool, including the prompts submitted.
Generation Date: Record the date the content was generated.
Review Steps: Outline the steps taken to review the content for accuracy and bias.
Additionally, be transparent about your use of generative AI by including a proper attribution statement within the OER. For example, "All images were generated by DALL-E3 and are in the Public Domain." This ensures clarity and transparency for users of your OER.
*Use the AI-Generated Content Tracker (Template) to systematically document the use of generative AI in your OER.
4. Respect Copyright and Intellectual Property
4. Respect Copyright and Intellectual Property
As previously mentioned, there is still uncertainty surrounding copyright and generative AI. During this period of uncertainty, OER creators should exercise caution with both the input given to AI and the output received to avoid accidental copyright infringements. (Note: The following guidelines should not be considered legal advice. When in doubt, consult a local copyright expert.)
Input considerations:
Avoid using copyrighted materials as input for AI, especially if it is done without permission.
Avoid prompting a generative AI tool to create content that includes trademarked or patented elements. For example, do not generate images featuring trademarked corporate logos unless you are doing so under the purposes of Fair Use.
Output considerations:
Avoid applying a CC license to entirely AI-generated content.
Always check the usage and ownership of output according to the tool's Terms of Service.
Assess using your due diligence whether or not the output of the generative AI tool explicitly infringes on copyright.
For Text: Search the web to confirm that the generated language does not match existing copyrighted content.
For Images: Use reverse image search tools like TinEye or Google Images to verify that the generated images do not duplicate copyrighted images.
Evaluating the Benefits and Challenges: The Case of Learning Analytics
Evaluating the Benefits and Challenges: The Case of Learning Analytics
The integration of artificial intelligence (AI), particularly learning analytics, in the creation of Open Educational Resources (OER) offers numerous benefits for educators, researchers, students, and instructional designers. However, while these technologies enhance OER, it is crucial to maintain a balanced perspective to avoid over-reliance on learning analytics. This balanced approach ensures that the human element in education is not overlooked.
Learning Analytics in OER Creation
Learning Analytics in OER Creation
Learning analytics involves the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. When applied to OER, learning analytics can provide insights into how educational materials are used, how students engage with content, and what improvements can be made to enhance learning outcomes.
Importance in the Creation Process
Importance in the Creation Process
1. Data-Driven Decision Making: Learning analytics enables educators to create OER based on empirical data. For example, by analyzing student performance and engagement data, educators can identify which topics are most challenging for students and create targeted resources to address these areas. This approach aligns with the findings of Bienkowski, M., Feng, M., and Means, B. (2012), who suggested that data-driven strategies can significantly enhance educational outcomes.
2. Personalized Learning: AI can help tailor OER to meet the individual needs of students. Adaptive learning technologies use learning analytics to provide personalized content recommendations, ensuring that each student receives resources that match their learning pace and style (Dawson, S., Gašević, D., Siemens, G., & Joksimović, S., 2014).
3. Continuous Improvement: By continuously collecting data on how OER is used, educators can iteratively improve the resources. For instance, if analytics show that a particular section of a resource is frequently revisited, it may indicate that students find it difficult and that further clarification or additional materials are needed.
Risks of Over-Reliance on Learning Analytics
Risks of Over-Reliance on Learning Analytics
1. Data Privacy and Security: The collection and use of learner data raise concerns about privacy and security. It is essential to ensure that data is anonymized and that robust security measures are in place to protect sensitive information (Slade & Prinsloo, 2013).
2. Loss of Human Touch: Over-reliance on learning analytics can lead to a mechanistic approach to education, where the nuances of human interaction and the importance of educator judgment are diminished. Educators must balance data-driven insights with their professional expertise to provide a holistic learning experience (Knox, 2017).
3. Bias and Fairness: AI algorithms can perpetuate existing biases in educational data, leading to unfair treatment of certain student groups. It is crucial to ensure that the data used for learning analytics is representative and that algorithms are regularly audited for bias (Williamson, 2016).
Practical Examples and Tools
Practical Examples and Tools
1. Learning Management Systems (LMS): Platforms like Moodle and Canvas integrate learning analytics tools that track student progress, engagement, and performance. Educators can use these insights to adapt their teaching strategies and improve OER content.
2. Adaptive Learning Platforms: Tools like Knewton and Smart Sparrow use AI to deliver personalized learning experiences. These platforms adjust the difficulty and type of content based on student performance data.
3. Intelligent Tutoring Systems (ITS): Systems like Carnegie Learning and ALEKS provide automated, personalized tutoring by leveraging learning analytics to understand and respond to student needs in real-time.
Benefits of using AI for OER Section_Learning Analytics.docxEvaluating the Benefits and Challenges: Business/Marketing Class Examples
Evaluating the Benefits and Challenges: Business/Marketing Class Examples
Using AI for OER has many benefits. As educators, leveraging this emerging technology can enhance the learning experience and outcomes for students, but it may be helpful to be aware of the challenges. The following resources provide a high level overview of ways in which AI presents benefits and challenges when used in Business/Marketing classes for educators at the high school/university level.
This presentation outlines the following benefits for consideration:
Personalized Learning
Interactive Simulations
Virtual Teaching Assistants
Data Analysis and Insights
Automated Grading and Feedback
Benefits of Using AI for OER: Business/Marketing Class Examples for Educators.pptxThis presentation outlines the following challenges for consideration:
Technical Expertise
Cost and Resources
Data Privacy and Security
Bias and Fairness
Adaptability and Acceptance
Challenges of Using AI for OER: Business/Marketing Class Examples for Educators.pptxReferences
References
Affordable Learning Georgia. (n.d.). Guidelines for using generative AI tools in Open Educational Resources. https://www.affordablelearninggeorgia.org/resources/create/opengenai/
Al-Smadi, M., Qawasmeh, O., Talafha, F., & Qasem, A. (2019). Using AI and NLP to generate educational content. International Journal of Artificial Intelligence in Education, 29(2), 145-161.
BCcampus OER Production Team. (2021). Getting started: OER publishing at BCcampus. BCcampus. https://opentextbc.ca/gettingstarted/
Bobula, M. (2024). Generative artificial intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications. Journal of Learning Development in Higher Education, (30). https://doi.org/10.47408/jldhe.vi30.1137
Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. U.S. Department of Education.
Dawson, S., Gašević, D., Siemens, G., & Joksimović, S. (2014). Current state and future trends: A survey of learning analytics. Journal of Educational Technology & Society, 17(3), 302-315
Knox, J. (2017). Data-driven education and the redesign of learning spaces. International Journal of Educational Research, 84, 153-163.
Sag, M. (2023). Copyright safety for generative AI. Houston Law Review, 61(2). https://houstonlawreview.org/article/92126-copyright-safety-for-generative-ai
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529.
TCC Libraries. (n.d.). Creating OER with AI - ChatGPT and AI Tools Faculty Research Guide. Research Guides at Tidewater Community College. https://libguides.tcc.edu/c.php?g=1313261&p=10209997
U.S. Copyright Office, Library of Congress. (2023). Copyright Registration Guidance: Works Containing Materials Generated by Artificial Intelligence (88 FR. 16190). https://copyright.gov/ai/ai_policy_guidance.pdf
Williamson, B. (2016). Digital education governance: Data visualization, predictive analytics, and 'real-time' learning. Journal of Education Policy, 31(2), 123-141.
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