AI Powered Learning: A Catalyst for Preservation and Revitalization of Endangered Languages

Cite this article as: Kareem F.T. & AbdulRahman A.A. (2025). AI Powered Learning: A Catalyst for Preservation and Revitalization of Endangered Languages. Zamfara International Journal of Humanities,3(3), 55-61. www.doi.org/10.36349/zamijoh.2025.v03i03.007

AI POWERED LEARNING: A CATALYST FOR PRESERVATION AND REVITALIZATION OF ENDANGERED LANGUAGES

Fatai Toyin Kareem, Ph.D
Department of Arts and Science Education
Kwara State University, Malete

And

Abdulwaheed Abiodun AbdulRahman, Ph.D
Department of English and Linguistics
Kwara State University, Malete

Abstract: This paper delves into how AI-powered tools can breathe new life into endangered languages, helping to preserve and revitalize them in ways that were previously unimaginable. Technologies like machine translation, speech recognition, and adaptive learning platforms are proving essential by simplifying language documentation and making language learning more engaging and accessible. Our findings show that AI can speed up data collection, increase global access to these languages, and encourage interactive, personalized learning. However, challenges remain: data for these languages is often limited, and ethical questions around cultural ownership arise. The study highlights that meaningful partnerships with indigenous communities and an ethical approach to AI are crucial. By building inclusive datasets and designing culturally sensitive AI tools, we can harness the power of technology to protect and celebrate linguistic diversity, giving endangered languages a lasting place in the digital world.

Keywords: AI-Powered Learning, Endangered Languages, Preservation, Revitalization, Speech Recognition

1. Introduction

Language is not just a means of communication; it encapsulates a community’s history, worldview, and cultural identity. The United Nations Educational, Scientific and Cultural Organization (UNESCO) estimates that nearly 50% of the world’s 7,000 languages are at risk of disappearing by the end of this century, with one language dying approximately every two weeks (UNESCO, 2020). This alarming statistic has drawn global attention to the urgency of language preservation, especially for indigenous and minority languages that hold unique cultural and historical significance (Harrison, 2010). For the communities that lose their language, there is often an accompanying loss of identity, traditional knowledge, and intergenerational wisdom, making language extinction a deeply impactful process on both individual and communal levels (Austin & Sallabank, 2017).

Historically, language preservation efforts have relied on documentation through written records, audio-visual recordings, and community-led language programs. However, these methods are often constrained by limited financial resources, personnel, and time. This is especially true for languages with few native speakers, whose knowledge might not be easily accessible or transferrable through traditional methods (Grenoble & Whaley, 2021). In recent years, artificial intelligence (AI) has emerged as a powerful tool that can augment and enhance these preservation efforts, offering scalable and efficient solutions that address some of the limitations of conventional approaches (Berment, 2022). By employing advanced technologies such as natural language processing (NLP), machine learning, and automated speech recognition, AI can facilitate the preservation and revitalization of endangered languages, potentially reaching a wider audience and fostering engagement in diverse communities (Lopez, 2022).

One of the significant advantages AI offers in language preservation is its capacity to process and analyze large datasets rapidly, which is essential for languages with limited written resources. For example, AI-driven translation models, like those developed by Google and Microsoft, can quickly create digital translations, increasing the visibility and accessibility of endangered languages on digital platforms (Gershgorn, 2020). Additionally, AI-powered language learning applications, such as Duolingo and Memrise, now include endangered languages like Hawaiian and Navajo in their offerings. These platforms use adaptive learning algorithms to cater to individual learning needs, making language acquisition accessible to a global audience and encouraging language use beyond native-speaking communities (Duolingo, 2023).

Furthermore, AI’s role extends beyond documentation and translation to immersive language learning experiences. Interactive AI systems, including chatbots and virtual tutors, can simulate real-life conversations and provide immediate feedback, which is critical for users learning a new language (Brown & Tsegaye, 2022). This interactive approach aligns closely with immersion-based language acquisition, traditionally considered one of the most effective methods of learning. By simulating conversational practice, AI not only aids learners but also contributes to the revitalization of endangered languages in a modern, engaging format.

In this paper, the role of AI in preserving and revitalizing endangered languages will be examined, with a particular focus on its impact in documentation, language learning, and overcoming challenges associated with language revitalization. As AI technology continues to evolve, it holds the potential to prevent linguistic and cultural erosion, making it a crucial tool in the ongoing global effort to preserve linguistic diversity.

2.0    Literature Review

Endangered Languages and the Need for Revitalization Languages are integral to the identity of communities, encapsulating their worldviews, histories, and cultural practices. However, rapid globalization, cultural assimilation, and economic pressures have contributed to the decline of many indigenous and minority languages (Grenoble & Whaley, 2021). Researchers have identified that once a language becomes endangered, there is a sharp decline in native speakers, leading to the gradual erosion of its linguistic complexities. The death of a language results in the loss of irreplaceable cultural and intellectual heritage (Harrison, 2010).

According to Austin and Sallabank (2017), revitalization efforts have traditionally relied on language documentation, community-driven educational programs, and government initiatives. These methods, while essential, are often limited by financial resources, human expertise, and time. In this context, AI emerges as a powerful tool that can enhance and scale language preservation efforts. AI technologies can help streamline the creation of digital dictionaries, language learning applications, and immersive experiences for users interested in learning endangered languages (Berment, 2022).

AI applications have proven invaluable in three primary areas of language preservation: documentation, learning, and revitalization. By leveraging technologies like machine translation, automated speech synthesis, and adaptive language learning, AI not only supports preservation efforts but also makes endangered languages accessible to new generations and broader audiences.

2.1. Machine Translation and Language Documentation

Machine translation has made remarkable strides in supporting endangered languages by increasing accessibility and enabling seamless communication across language barriers. Google and Microsoft, for instance, have developed AI-driven translation models that extend support to languages like Hawaiian, Maori, and Quechua. These models process large datasets, identifying linguistic patterns and generating translations between endangered languages and widely spoken languages like English or Spanish (Lopez, 2022). While perfect accuracy is challenging, AI models benefit from continuous improvements based on feedback from native speakers, researchers, and new datasets. This adaptability allows the models to better represent the nuances of endangered languages and bridge gaps in understanding (Gershgorn, 2020).

In addition, machine translation aids in documentation by creating digital records of languages with limited written resources. AI can analyze complex linguistic structures and dialects more efficiently than traditional methods. Tools like Google’s Neural Machine Translation have facilitated the preservation of regional dialects by providing digital translations that would otherwise be too costly and time-consuming to document through manual transcription (Koehn, 2020). Such AI technologies are particularly useful for languages that lack formalized grammar rules or standardized spelling, as they can analyze and learn from varying language structures and speech patterns.

2.1.1 Automated Speech Recognition and Synthesis

For many endangered languages that exist primarily as oral traditions, AI-powered speech recognition and synthesis technologies are crucial. These tools enable linguists to record and analyze spoken language with minimal distortion, preserving phonetic and syntactical variations essential to a language's identity. AI models can categorize and label phonetic data efficiently, allowing researchers to create comprehensive speech datasets that are valuable for future study (Bansal, Zhang, & Kaminski, 2023). Automated speech synthesis also helps recreate spoken language from text, making it possible to generate immersive language learning experiences even if fluent speakers are scarce. Tools like Mozilla's Common Voice initiative collect and analyze voice data from indigenous language speakers, contributing to robust, publicly accessible speech datasets that support both academic research and community use (Mozilla, 2

2.1.2 AI in Language Learning Applications

AI-powered language learning applications such as Duolingo and Memrise have incorporated endangered languages, providing tailored learning experiences that adapt to users' proficiency and pace. These applications use adaptive algorithms to customize vocabulary practice, sentence structure exercises, and pronunciation correction, making the learning experience personalized and engaging (Duolingo, 2023). Language learning AI not only addresses individual learner needs but also fosters community engagement, drawing attention to the language and inviting non-native speakers to participate in its preservation.

Another important development in AI-driven language learning is the use of chatbots and virtual tutors. These AI entities enable conversational practice, allowing learners to engage with language in a dynamic, interactive way. For example, Google’s Project Euphonia utilizes AI to create chatbots that mimic conversational exchanges in indigenous languages, offering learners an immersive experience that closely resembles interaction with a native speaker (Google, 2023). The ability to engage in dialogue practice is critical for language retention and encourages learners to apply language skills actively, which is often essential for language revitalization.

By focusing on documentation, learning, and real-time interaction, AI-powered solutions offer diverse and robust support for endangered languages, creating digital platforms that ensure these languages are not only preserved but revitalized in a modern context. As AI technology continues to advance, these tools will likely become even more integral to global efforts aimed at preserving linguistic diversity.

2.2 Challenges in AI-Powered Language Revitalization

While AI offers transformative tools for language preservation and revitalization, it also faces significant challenges that impact its effectiveness, ethical considerations, and overall feasibility. These challenges stem from data limitations, ethical concerns around cultural ownership, and the constraints of AI technology itself.

2.2.1 Data Scarcity and Quality Concerns

 One of the foremost challenges in AI-driven language revitalization is the scarcity of high-quality data for endangered languages. Unlike widely spoken languages that have extensive datasets, endangered languages often lack sufficient written and audio resources for AI models to learn effectively (Mager, Garrette, & Loaiza, 2018). Many endangered languages are oral, with limited or no formalized writing systems, which restricts the availability of text data and necessitates costly fieldwork to collect and process spoken language samples (Berment, 2022). This limitation affects the accuracy and reliability of AI models, as the lack of data can result in incomplete or skewed representations of linguistic structures. Consequently, AI systems developed with insufficient data may produce suboptimal results, missing essential dialectal or phonetic variations that characterize the language (Bansal, Zhang, & Kaminski, 2023).

2.2.2 Cultural and Ethical Concerns

The ethical implications of using AI for endangered language revitalization are complex. Language is deeply intertwined with cultural identity, and external interventions in language preservation may lead to concerns about cultural commodification, ownership, and exploitation. Large technology corporations and research institutions often drive AI-based language projects, which may not always prioritize the cultural integrity or autonomy of indigenous communities (Dailey, 2020). This raises questions about who owns the digital representations of these languages and who should control access to AI models built with indigenous language data. Indigenous and minority communities may fear that their linguistic heritage could be commercialized or misrepresented by outsiders, especially if AI tools are developed without adequate consultation and partnership with native speakers (Roche, Maruyama, & Lowe, 2021).

To address these issues, there is a need for a collaborative approach that actively involves the language communities in both data collection and the application of AI technologies. Community involvement not only helps ensure that AI systems respect cultural values but also facilitates the development of tools that align with the communities’ needs and goals for language revitalization.

2.2.3 Technical and Financial Constraints

The development of AI models for language preservation also demands substantial technical resources and funding. Training AI systems requires powerful computational infrastructure, which can be prohibitively expensive and is often concentrated within major corporations or well-funded institutions. For endangered languages spoken in remote or economically disadvantaged regions, obtaining the necessary funding and technological resources to build and maintain AI models is particularly challenging (Lopez, 2022). Additionally, without adequate funding, AI models may not receive the continuous updates needed to improve accuracy and incorporate community feedback. This can result in outdated or incomplete models that fail to accurately represent the language, potentially leading to linguistic inaccuracies that could undermine revitalization efforts (Nicholas, Baker & Leary, 2021).

Moreover, AI technologies are primarily developed for languages with extensive global use, and adaptations for minority languages often lag behind. For instance, the standard algorithms used in natural language processing and machine translation are designed to process languages with extensive datasets, which limits their adaptability to the unique phonetic, morphological, and syntactical elements found in endangered languages (Koehn, 2020). This technological gap underscores the need for specialized, inclusive AI models that can accommodate diverse linguistic structures.

Addressing the Challenges Overcoming these challenges requires a multi-faceted approach, involving partnerships between technologists, linguists, and language communities. Collaborative frameworks, such as those implemented in the First Peoples’ Cultural Council’s AI initiatives, demonstrate the potential for culturally respectful and community-centered language preservation efforts (FPCC, 2023). However, ongoing support from both public and private sectors is essential to address the financial and technological limitations hindering broader AI application in endangered language revitalization.

3.0   Discussion

AI technology has significantly expanded the tools available for the preservation and revitalization of endangered languages, offering solutions to many challenges faced by traditional approaches. By leveraging AI-powered platforms, endangered languages can be better documented, taught, and even integrated into modern digital platforms, thereby enhancing their accessibility and usage.

3.1 AI and Digital Documentation

One of the most critical components of language preservation is comprehensive documentation. Traditional methods involve field linguists collecting spoken word samples, grammatical structures, and vocabulary lists from native speakers, often a time-consuming and resource-intensive process. AI, however, accelerates this process by automating data collection, analysis, and cataloging. For instance, advanced AI algorithms can sift through large sets of linguistic data and identify patterns, creating structured datasets for further study (Mager Garrette & Loaiza, 2018). This is particularly useful for languages with limited written records or those that rely primarily on oral transmission.

An example of AI in digital documentation is the work being done on the Quechua language in South America. Microsoft partnered with researchers to build AI models that can translate spoken Quechua into Spanish, thereby creating a digital record of spoken Quechua (Gershgorn, 2020). This approach not only preserves the language but also makes it accessible to a broader audience through technology. AI-powered transcription systems have made it easier to process large volumes of linguistic data, which would have otherwise taken years to manually catalog.

Moreover, the use of natural language processing (NLP) in AI can help identify underrepresented phonetic and syntactic structures. For example, some AI models have been trained to detect dialectical differences within endangered languages, creating a more comprehensive understanding of the language and its variations (Bansal, Zhang & Kaminski 2023). This is crucial for languages with diverse dialects, where slight variations may indicate important cultural distinctions.

3.1.2 AI in Language Learning and Revitalization

The ability of AI to support language revitalization is perhaps most evident in the development of personalized language learning tools. Language learning platforms powered by AI, such as Duolingo, Memrise, and Babbel, have recently begun to include endangered languages in their offerings. These platforms leverage AI to create adaptive learning environments that cater to individual learners' needs. For instance, Duolingo's AI algorithms assess user performance and adjust the difficulty of lessons accordingly, ensuring learners can proceed at their own pace while receiving feedback tailored to their progress (Duolingo, 2023).

An interesting case study is the integration of Hawaiian and Navajo languages on Duolingo. In both instances, AI was used to structure the learning curriculum, offering interactive lessons that teach vocabulary, grammar, and sentence structures. These platforms provide a global audience access to endangered languages, democratizing the learning process and making it easier for individuals, both within and outside the native communities, to contribute to the language’s revival.

In addition to structured learning programs, AI-powered chatbots offer an immersive language learning experience. Unlike traditional methods that may rely on pre-recorded dialogues, chatbots can engage users in dynamic conversations, responding in real-time to inputs and correcting mistakes as they occur (Brown & Tsegaye, 2022). This interaction mirrors conversational practice with native speakers, providing invaluable language exposure. As language use is key to revitalization, AI-facilitated immersion helps individuals to practice endangered languages in everyday contexts, fostering active use of the language.

3.1.3 Technological and Ethical Challenges

While AI presents promising opportunities, its integration into language preservation raises several challenges. One of the primary technological challenges is the availability of sufficient data. Most AI systems, especially those involved in machine learning, require extensive datasets to function effectively. However, many endangered languages lack sufficient written or recorded material to support the development of these systems. This lack of data limits the accuracy of AI models and their ability to fully capture the linguistic nuances of endangered languages (Mager Garrette & Loaiza., 2018).

Additionally, there are ethical concerns regarding the involvement of AI in preserving indigenous and minority languages. Language is inherently tied to cultural identity, and there are fears that external intervention through AI could lead to cultural commodification or misrepresentation. When large corporations or institutions become involved in language preservation efforts, the question arises of who truly owns the language and its digital representation. Linguistic and cultural sovereignty must be maintained to ensure that AI is used in a way that respects the wishes and interests of the communities whose languages are at risk (Dailey, 2020).

Moreover, there is the potential for AI technologies to be misused in ways that undermine language preservation efforts. Commercial interests might prioritize the more profitable aspects of language revitalization (e.g., gamified learning platforms) while neglecting more critical areas such as community-led efforts and the preservation of dialectical variations. To mitigate these risks, it is essential that language communities are not only consulted but are actively involved in developing and implementing AI-powered solutions.

3.1.4 Collaborative Approaches to AI and Language Preservation

 The most successful AI-driven language preservation projects are those that involve close collaboration between AI developers, linguists, and native speaker communities. By ensuring that native speakers have an active role in shaping AI tools, it is possible to create more culturally sensitive and accurate resources.

One example of such collaboration is the First Peoples' Cultural Council (FPCC) project in Canada. The FPCC partnered with AI researchers to develop digital resources that preserve and teach indigenous languages like Secwepemctsín and Halkomelem. This project involved ongoing dialogue with indigenous communities to ensure the tools developed were aligned with their cultural and educational goals (FPCC, 2023). This type of collaboration should be a model for future AI-driven language preservation efforts, as it ensures that technological innovation is guided by the needs and values of the communities involved.

4.0 Conclusion

AI-powered learning represents a transformative tool in the fight to preserve and revitalize endangered languages, offering unprecedented advancements in language documentation, learning, and usage. By enhancing the speed and accuracy of recording linguistic data, creating personalized learning experiences, and providing immersive conversational practice, AI can help overcome key obstacles in traditional language preservation methods. However, the challenges of data scarcity, ethical considerations, and technical limitations remind us that a balanced approach is essential.

To unlock AI’s full potential, future initiatives must focus on developing inclusive datasets that capture the nuances of diverse languages, especially those with minimal recorded material.

Additionally, collaborative efforts between technologists, linguists, and indigenous communities are essential to ensure that AI tools are not only accurate but also culturally sensitive. With sustained innovation and cross-cultural partnership, AI has the potential to revitalize endangered languages in the digital age, preserving invaluable linguistic and cultural heritage for generations to come.

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