Author Note: Correspondence concerning this paper should be addressed to Aigerim Shilibekova, Simon Fraser University at [email protected].

Disclaimer: The views expressed in this paper are solely those of the author and do not reflect the views of Simon Fraser University, University Canada West, or any affiliated institutions.

Abstract

In an increasingly interconnected world, education must evolve to address the diverse needs of learners, particularly where linguistic, cultural, and technological barriers limit access to high-quality, inclusive education. This paper presents an approach to these challenges through an AI-powered Universal Design for Learning (UDL) course developed as an Open Educational Resource (OER) for Kazakh-speaking educators at Atyrau State University in Kazakhstan. The course leverages advanced AI tools to create culturally relevant and accessible learning materials that effectively bridge these barriers.

By integrating UDL principles into both content and design, this approach introduces a meta-layer of UDL that enhances inclusivity for a diverse range of learners. Utilizing the Successive Approximation Model (SAM), the course underwent iterative refinement, achieving a 95% completion rate among participants. Key findings highlight the significance of culturally aligned content, the complexities of AI-driven localization, and the scalability of AI-enhanced OER for professional development. This paper illustrates how AI-powered OER can foster equity and inclusivity in education, offering a replicable model for transforming professional development in diverse and underserved educational contexts globally.

Keywords: universal design for learning (UDL), artificial intelligence (AI), professional development, open educational resources (OER), AI-enhanced OER.

Imagine a faculty member in a regional city in Kazakhstan striving to create an inclusive classroom despite limited access to resources in their native language. This scenario exemplifies the broader challenges faced by educators in non-English-speaking regions, where linguistic barriers, scarcity of localized materials, and technological inequities hinder the adoption of transformative teaching frameworks such as Universal Design for Learning (UDL). A review by CBM Global Disability Inclusion (2021) highlights that UDL has primarily been developed in high-income countries, where technology plays a central role, raising concerns about its applicability in low- and middle-income countries (LMICs) with constrained resources. This underscores the need for contextualized approaches to ensure the framework’s relevance and effectiveness.

The integration of Artificial Intelligence (AI) and Open Educational Resources (OER) offers a promising pathway to address these challenges. By enabling the creation of culturally and linguistically relevant educational materials, AI-powered tools such as ChatGPT, Google Translate, DALL-E, LUDIA, and Kazakh GPT provide educators with unprecedented opportunities to bridge resource gaps. These tools facilitate refined translations, culturally tailored visuals, and interactive, accessible content, significantly enhancing engagement and usability.

This rapid response paper explores the design, implementation, and outcomes of an AI-enhanced UDL course developed as an OER for Kazakh-speaking educators. Grounded in the theoretical foundations of UDL and open educational practices, the course confronts critical linguistic, cultural, and technological challenges. It showcases the transformative potential of AI-enhanced OER in democratizing professional development and provides a framework that can be replicated in similar contexts. The findings illuminate the implications of using AI for localized educational content and highlight areas for future research and development in the OER ecosystem.

Challenges in Faculty Professional Development

The professional development of educators in non-English-speaking regions, such as Kazakhstan, faces multiple challenges that hinder the adoption of inclusive teaching practices. These challenges span linguistic barriers, resource gaps, technological constraints, and equity issues, all of which demand innovative, context-sensitive solutions.

Linguistic Barriers

The lack of UDL resources in the Kazakh language presents a critical challenge, as most materials are designed for English-speaking audiences. Translating these resources while preserving their pedagogical integrity requires more than direct translation. Cultural adaptation is essential to ensure materials resonate with local educational contexts. Without such adaptation, educators face difficulties connecting inclusive teaching frameworks to their unique needs. AI tools like ChatGPT offer potential solutions by refining translations and adapting content to reflect cultural nuances, enhancing accessibility and engagement (Mills et al., 2023). However, localized validation and further development are required to meet Kazakhstan’s specific educational demands.

Resource Gaps

Kazakhstan’s reliance on English-centric OER underscores a critical deficiency in localized materials aligned with UDL principles. Such reliance restricts educators’ access to culturally and pedagogically relevant content, hampering efforts to implement inclusive teaching practices effectively. Localization, a process vital to making OER contextually appropriate, is often impeded by linguistic and technological barriers, as observed in the Global South (Bradshaw & McDonald, 2023). Generative AI tools like ChatGPT offer significant potential to bridge these gaps by enabling rapid co-creation and adaptation of resources tailored to regional needs. However, successful localization requires institutional commitment to fostering collaborative frameworks and empowering educators with the necessary skills and resources.

Technological Constraints

Infrastructure disparities, especially in rural areas, represent a critical barrier to faculty development in Kazakhstan. Limited internet connectivity, outdated devices, and inadequate IT support restrict educators’ ability to engage with professional learning opportunities and leverage AI tools effectively. Additionally, the absence of localized interfaces for AI platforms further compounds these challenges, leaving many educators unable to integrate digital solutions into their teaching practices. Addressing these constraints necessitates systemic investments in digital infrastructure and policies promoting comprehensive digital literacy training. As highlighted by Zholdigaly et al. (2024), AI technologies have the potential to revolutionize education in Kazakhstan, but their full impact will depend on bridging the digital divide and ensuring equitable access to the necessary technological resources.

Equity Issues

Professional development opportunities are unevenly distributed across urban and rural areas, exacerbating systemic inequities. Educators in less urbanized regions face limited access to resources and institutional support, creating disparities in teaching quality. AI-enhanced OER provides a scalable solution by enabling the creation of inclusive and accessible training experiences tailored to diverse educator needs. As Ghaderi (2023) highlights, embedding equity in OER design ensures that educational materials address redistributive and recognitive justice, creating culturally relevant resources that meet the diverse needs of educators. Intentional instructional design, supported by institutional commitment, is critical to bridging these gaps and advancing educational equity.

By addressing these interlinked challenges through the integration of AI tools, localization of OER, and equity-focused policies, Kazakhstan can transform its professional development landscape. This approach not only aligns with UDL principles but also fosters scalable, inclusive solutions tailored to the unique needs of non-English-speaking educators.

The AI-Enhanced UDL Course: Design and Delivery

The AI-enhanced UDL course was developed as a core e-learning OER tailored for non-English-speaking faculty at Atyrau State University, Kazakhstan. Designed to address the challenges faced by Kazakh educators across diverse academic disciplines, the asynchronous course focused on fostering the adoption of inclusive teaching practices.

Course Design and Structure

The course followed a modular design organized into three comprehensive modules, each with three sections. This structure guided participants progressively from theoretical concepts to practical applications, allowing them to build their understanding step by step. The design was informed by the Successive Approximation Model (SAM), which emphasizes an iterative process and continuous feedback. This approach allowed for regular course adjustments based on participant input, ensuring the learning experience remained relevant and responsive. The flexibility of the asynchronous format enabled faculty to engage with the content at their own pace, balancing their teaching responsibilities with their professional development.

The course aimed to provide participants with a thorough understanding UDL principles and their importance in fostering inclusive learning environments. Additionally, it sought to equip faculty with practical, culturally relevant, and linguistically sensitive strategies for effectively incorporating UDL into their teaching practices.

The course comprised three modules: the first introduced UDL and its role in fostering inclusive education. The second focused on UDL’s core principles of engagement, representation, and action and expression, illustrating their application through culturally relevant examples tailored to Kazakhstan’s educational context. The final module emphasized practical implementation by guiding participants in designing inclusive lesson plans, assessments, and classroom strategies to promote hands-on engagement with the material.

Each module incorporated multimedia content, including video resources with Kazakh subtitles, audio files in Kazakh, knowledge check activities, and visuals generated using AI tools. These resources were tailored to support diverse learning styles and varying levels of technological proficiency. The use of AI-generated visuals, which reflected the local cultural context, ensured that the content was both engaging and contextually relevant.

Platform Selection and AI Integration

Articulate Rise was selected for its modular structure, multimedia capabilities, and accessibility features, making it convenient for a flexible, UDL-based course design. The university’s subscription to Articulate Rise provided cost-free access, facilitating course development without additional licensing fees. Once published, the course was available to educators under OER principles, enhancing accessibility, especially for underserved communities. However, the platform’s cost may hinder broader adoption in resource-constrained environments.

To address linguistic and cultural adaptation challenges, a comprehensive suite of AI tools was integrated into the course design to enhance localization and accessibility for UDL. ChatGPT, based on OpenAI’s GPT-4 model, served as the core tool for content design and refinement, improving initial drafts by enhancing translations and ensuring linguistic accuracy tailored to the Kazakh educational context. LUDIA, a UDL + AI chatbot powered by OpenAI’s Proprietary Engine, was utilized in Kazakh to create culturally relevant assessments and interactive learning content grounded in UDL principles. Google Translate provided initial translations into Kazakh, laying a foundation for localization. KazakhGPT, a localized chatbot, offered real-time support in Kazakh, answering questions about UDL principles, assisting with course navigation, and providing immediate feedback. DALL-E, OpenAI’s generative image model, was employed to create culturally resonant visuals, such as depictions of Kazakh classrooms and traditional practices. Canvaenhanced course visuals with professional-quality graphics, using AI-driven tools to help users create visually engaging materials quickly and efficiently, ensuring accessible resources. Finally, Amara, an open-source AI tool for video subtitling, was used to subtitle video materials, reinforcing UDL’s emphasis on multiple means of representation.

Thus, by combining Articulate Rise’s modular framework with AI-enhanced tools, the course provided a dynamic and culturally relevant learning experience. This integration of technology and pedagogy addressed linguistic and technological challenges while establishing a replicable model for equitable professional development in non-English-speaking contexts.

Lessons from Implementation

The implementation of the AI-enhanced UDL course highlighted its significant potential while revealing areas for improvement in leveraging technology for professional development. Achieving a 95% completion rate, the modular design provided flexibility and coherence, effectively accommodating educators’ diverse schedules and varying levels of expertise. The recent addition of a certificate of completion further enhanced faculty motivation, showcasing the value of tangible recognition in professional development.

The implementation demonstrated the capacity of generative AI to enhance localization efforts effectively. ChatGPT ensured linguistic and cultural accuracy in translations, while DALL-E and Canva produced visually appealing and contextually relevant materials. The integration of KazakhGPT into the course offered localized support, enriching the interactive learning experience for participants. Moreover, LUDIA, the UDL + AI chatbot, facilitated the navigation of course content and supported the effective presentation of UDL principles in Kazakh. Supporting this, Bozkurt (2023) emphasizes that AI technologies facilitate translation and localization, enabling the dissemination of OERs to a broader and more diverse audience.

This course exemplifies a meta-layer design, as it not only teaches UDL principles but also embodies them throughout its structure and delivery. Participant feedback indicated a high level of engagement and appreciation for the culturally relevant materials, highlighting the success of the localization efforts.

However, several challenges emerged during implementation:

Insufficient Training on AI Tools: Many participants reported feeling unprepared to effectively use AI tools like LUDIA and KazakhGPT. Providing comprehensive training sessions on these technologies could enhance educators’ confidence and engagement.

Cultural Context Misalignment: Despite efforts to tailor content for the Kazakh context, some materials felt generic and did not resonate deeply with local cultural nuances. More thorough consultation with local educators during content development could improve cultural relevance.

Limited Contextual Accuracy: Challenges such as the limited contextual accuracy of tools like Google Translate and the depth of KazakhGPT underscored the need for more robust AI systems and language-specific datasets.

Feedback Loop Limitations: Although the course design allowed for feedback, the mechanisms for collecting and implementing participant suggestions were not always effective. Establishing a more robust feedback system that actively incorporates input could enhance course content and delivery in future iterations.

These lessons emphasize the importance of combining technological innovation with institutional support to effectively scale AI-enhanced OER, creating inclusive and culturally relevant professional development opportunities for diverse educational settings. Ongoing collaboration with educators and continuous refinement of course content will be essential in adapting to emerging needs and further enhancing the impact of this professional development initiative.

Replication

To effectively replicate the AI-enhanced UDL course, a structured approach is essential. Use the SAM instructional design model to refine the course iteratively, incorporating participant feedback to address local needs and ensure relevance.

Flexible Modular Structure: Create a course framework supporting asynchronous, self-paced multimedia learning. This approach engages learners of varying technological proficiencies and includes adaptive assessments to personalize their experience.

Strategic Platform Selection: Choose interactive platforms like Articulate Rise for content delivery while remaining mindful of resource constraints. Consider alternatives such as Moodle or EdX to enhance accessibility.

AI Tools for Localization and Engagement: Utilize AI tools effectively: ChatGPT for linguistic accuracy, DALL-E for culturally relevant visuals, and KazakhGPT for localized support. LUDIA can enhance interactivity by providing real-time assistance during content creation.

Stakeholder Collaboration: Engage regional educators and institutions to validate course content, ensuring cultural relevance and fostering ownership for better local adoption.

Scalable Implementation: Tailor the course model for diverse, non-English-speaking regions by adjusting AI tools and strategies to meet local needs. Align with global UDL and OER principles to promote equity and accessibility.

Anticipate Challenges: Identify potential barriers, such as limited technology access or high platform costs. Propose solutions like institutional partnerships and leverage open-source platforms to address these challenges.

This framework may serve as a guide for replicating AI-enhanced UDL courses, promoting equity in professional development while ensuring scalability and inclusivity.

Results and Implications

Impact Metrics

The AI-enhanced UDL course achieved significant milestones, demonstrating its potential as a professional development model. The 95% completion rate among participants underscores the effectiveness of its modular, asynchronous design, which accommodated diverse schedules and technological proficiencies. Knowledge check scores improved from an average of 60% pre-course to 85% post-course, indicating an enhanced understanding of UDL principles. Notably, AI-generated visuals and localized content, including culturally resonant names and contextualized scenarios, enhanced engagement and connected meaningfully with the Kazakh-speaking audience. These preliminary outcomes demonstrate the course’s potential as a scalable and inclusive model for professional development in diverse educational contexts.

Broader Implications

Equity and Access

This course highlights the potential of AI-enhanced OER to democratize professional development for non-English-speaking educators. By addressing linguistic barriers and resource gaps, it empowered faculty in underserved regions, such as rural Kazakhstan, to access high-quality, culturally relevant training. Generative AI tools not only facilitate the creation of localized content but also enable the curation of personalized resources, fostering a more inclusive and equitable educational landscape (Bozkurt, 2023).

Scalability

The modular design and adherence to OER principles make the course highly adaptable to diverse contexts. AI tools like ChatGPT and LUDIA streamline localization and customization, enhancing scalability. However, reliance on proprietary platforms such as Articulate Rise presents challenges in cost and accessibility. Strategic solutions, such as partnerships with local educators, adoption of open-source alternatives, and pilot programs in varied cultural and linguistic settings, are essential to ensure scalability without compromising inclusivity (Bozkurt & Sharma, 2023).

AI and OER Synergy

This course demonstrates the transformative potential of integrating AI tools with OER to enhance educational inclusivity. According to Tila and Levy (2023), AI technologies like ChatGPT significantly enhance the process of creating and curating OER by enabling dynamic content creation, improving localization, and fostering critical student engagement.

Sustainability

The adaptive nature of AI-powered OER models offers a sustainable pathway for educational development. Iterative frameworks like the Successive Approximation Model (SAM), as cited by Craig et al. (2022), align well with Mills’ (2023) advocacy for continuous revision and reflection in Open Educational Practices (OEP). Embedding UDL within these initiatives can further enhance the inclusivity and efficacy of professional development programs by ensuring that they are accessible and beneficial to a diverse range of learners.

Final Thoughts

This paper highlights the transformative potential of AI-driven tools integrated with OER to address the unique challenges of professional development in non-English-speaking contexts. The AI-enhanced UDL course for Kazakh-speaking educators leverages AI tools like ChatGPT, DALL-E, Canva, and LUDIA to overcome linguistic, cultural, and accessibility barriers. The contextual innovation lies in the meta-layer design of using UDL principles to teach UDL, a novel approach that bridges theory and practice while addressing the specific needs of non-English-speaking educators. This dual alignment ensures that the course’s pedagogical practices resonate deeply with the learners’ context, fostering a more inclusive and impactful learning experience. However, the reliance on proprietary AI tools exposes tensions between the ethos of openness and the constraints of commercial technologies, underscoring the necessity for global collaboration to develop sustainable, open-source solutions that maintain quality and accessibility.

References

 Bozkurt, A. (2023). Generative AI, synthetic contents, open educational resources (OER), and open educational practices (OEP): A new front in the openness landscape. Open Praxis, 15(3), 178–184. https://doi.org/10.55982/openpraxis.15.3.579

Bozkurt, A., & Sharma, R. C. (2023). Generative AI and prompt engineering: The art of whispering to let the genie out of the algorithmic world. Asian Journal of Distance Education, 18(2). https://doi.org/10.5281/zenodo.8174941

Bradshaw, E. D., & McDonald, J. K. (2023). Informal practices of localizing open educational resources in Ghana. International Review of Research in Open and Distributed Learning, 24(2), 19–35. https://doi.org/10.5281/zenodo.601935

CBM Global Disability Inclusion. (2021). Universal Design for Learning: A review of the literature with a focus on LMICs. Retrieved from https://www.cbm.org/fileadmin/user_upload/UDL_review_report_2021.pdf

Ghaderi, S. (2023). Designing OER with equity: An example of situating equity in a community college statistics course redesign. Open Praxis, 15(3), 235–243. https://doi.org/10.55982/openpraxis.15.3.572

Mills, A., Bali, M., & Eaton, L. (2023). How do we respond to generative AI in education? Open educational practices give us a framework for an ongoing process. Journal of Applied Learning & Teaching, 16(1), 16–30. https://doi.org/10.37074/jalt.2023.6.1.34

Zholdigaly, B., Zhumabayeva, L. O., & Abdykerimova, E. A. (2024). Artificial intelligence in the education sector of Kazakhstan: Opportunities and prospects. Caspian University of Technology and Engineering, 1–10.