Artificial Intelligence (AI) is reshaping language education by providing personalized, accessible, and scalable learning opportunities. This topic is increasingly important as the global demand for bilingual and multilingual individuals continues to increase, with learners seeking effective and flexible options that fit their schedules and learning styles. Traditional language learning often requires a consistent classroom setting, human instructors, and time-bound courses, which can be challenging for learners with limited resources or diverse educational needs. However, with advancements in AI-driven tools, language learners now have access to applications that provide instant feedback, personalized lessons, and even conversational practice, a feat that would have been challenging, if not impossible, to achieve in traditional settings. Understanding AI’s role in language learning today is essential for educators, curriculum designers, and policymakers who seek to incorporate effective, cutting-edge technology into language instruction.

My interest in AI-driven language learning stems from my experiences as an adult language learner. Growing up in a multicultural household and working with diverse learners in both academic and professional settings, I have witnessed the challenges of language acquisition, especially for students with limited access to resources. This personal connection motivates my exploration of how AI can bridge these gaps and offer equitable and engaging language education opportunities for all learners.

The topic of AI in language instruction has several core dimensions. The first is personalization since AI can tailor lessons to each learner’s skill level and preferences. Applications like Duolingo offer premium services that use AI algorithms to customize learning pathways, adjusting vocabulary and grammar exercises based on a user’s strengths and weaknesses. The second dimension is accessibility, which AI-enhanced platforms promote by making language learning available to users with an internet connection at any time and place. For people in areas with limited resources, this access can be potentially life-changing as it provides them with tools that were previously unavailable.

In researching AI’s application in language learning, it is essential to explore literature from fields such as educational technology, linguistics, second language acquisition, and AI ethics. Influential theories in language education, like Vygotsky’s Sociocultural Theory and Krashen’s Input Hypothesis, provide frameworks for examining how AI tools can support language acquisition through interaction and comprehensible input (Krashen, 1982). Additionally, research from other education journals explores the adaptive and interactive capabilities of AI in language education, discussing how AI can either support or potentially hinder the learning process based on how it is implemented. Studies examining the ethics of AI in education also highlight important considerations, such as bias in language datasets and the potential for AI to standardize language in ways that may overlook regional dialects and cultural nuances. By examining both the promise and limitations of AI in language learning, this research aims to present a balanced view of how AI could shape the future of language education and the challenges that must be addressed to fully realize its potential.

Constructivism and Sociocultural Theory

Constructivism and sociocultural theories provide a foundation for understanding how AI can be used effectively in language education. Constructivism (Fig. 1) suggests that learners actively construct knowledge based on their experiences, while sociocultural theory emphasizes the role of social interaction in cognitive development (Vygotsky, 1934/1986). Lev Vygotsky, a prominent figure in sociocultural theory, argued that language acquisition is not only a cognitive process but also a social one, where language skills develop through social interactions (Vygotsky, 1934/1986). In this context, AI applications like chatbots and virtual language partners can serve as interactive tools, allowing learners to engage in conversation and mimic the social aspects of language learning.

Fig. 1: Mangino, M. E. (n.d.). Constructivism. https://manginodesign.mit.edu/comparing-learning-theories/constructivism

The Input Hypothesis

Stephen Krashen’s Input Hypothesis (Fig. 2) is central to language acquisition theories related to AI. Krashen’s theory argues that language learners must be exposed to comprehensible input, or language that is just above their current proficiency level, to progress (Krashen, 1982). AI’s potential lies in its ability to adapt content to an individual learner’s proficiency level, offering “i+1” input, a concept that is central to Krashen’s theory. Many AI-driven platforms apply this by continuously assessing a learner’s progress and adjusting lesson difficulty accordingly. However, as we will see, critics argue that AI-driven input lacks the cultural context necessary for a comprehensive language learning experience.

Fig. 2: Krashen's Input Hypothesis: (Sivagnanam & Yunus, 2020)

Behaviorism and Reinforcement Learning

Behaviorism, specifically Skinner’s theories on reinforcement, also plays a role in AI applications for language learning. Behaviorist theory suggests that learners acquire new behaviors through reinforcement, a concept that AI apps use by offering rewards, such as points or badges, for completing tasks. Duolingo is an example of an app that incorporates these gamification elements to keep learners engaged through positive feedback (Huynh et al., 2016). For instance, if a learner answers a question incorrectly, the app will recycle the question for review at the end of the lesson, and it will continue to reappear until the learner provides the correct response, reinforcing desired behaviors. Examples of Duolingo’s gamification rewards that keep users motivated are highlighted below (Fig. 3).

I have been using Duolingo for several years, and, in my opinion, Duolingo excels at generating consistent app usage through rewards and recognition. The platform not only tracks milestones but also integrates social elements, such as weekly league competitions where users can connect with others. Achievements are shared to an in-app network which encourages mutual support and motivation.

Fig. 3: Advincula, C. J. (2024). Duolingo Reward System. https://ling-app.com/tips/duolingo-review/

Criticisms of AI in Language Education

Despite its promise, AI-driven language learning faces criticisms from scholars concerned about cultural authenticity, over-standardization, and ethical implications.

1. Loss of Cultural Authenticity

Critics argue that AI’s inability to convey cultural nuances embedded in language interactions limits its effectiveness. Without cultural context, language learning becomes a robotic process that is unable to prepare learners for real-world communication. Michail Fountoulakis (2024) warns that while AI can provide learners with structural language practice, it lacks the cultural immersion necessary for meaningful language acquisition.

For instance, idioms—often used in everyday conversation—do not always have direct translations and require contextual understanding to be used correctly. Mastering idioms involves more than memorizing phrases, it requires exposure to real interactions where learners can observe tone, body language, and situational context, all of which are crucial in grasping appropriate usage. The video below expands on this concept, emphasizing that cultural knowledge is vital in language learning. Without an understanding of the cultural mindset and perspectives unique to the target language, learners risk misinterpreting or misusing phrases in daily interactions which can impact their ability to communicate effectively. As the speaker notes, language learning involves more than translation; it is about adapting to the cultural contexts that shape how meaning is created and conveyed in conversation.

Video 1: Metatronacademy. (2023). Why Culture is So Important When Learning a New Language [Video]. YouTube. https://www.youtube.com/watch?v=fE-xiTXCCqk

When learning Spanish, I encountered the phrase “ponte las pilas,” which literally translates to “put the batteries in.” This phrase is commonly used to tell someone to “work harder” which is not immediately obvious without cultural context. I was able to learn its proper usage by observing its conversations and noticing how people responded, which provided the necessary situational cues. Without that context, I would have struggled to grasp its meaning.

This example further highlights one of the limitations of AI in language learning—while AI tools can teach vocabulary and grammar, they often fall short in conveying cultural nuances and real-world usage. To address this gap, educators should incorporate opportunities for students to have authentic interactions in the target language. AI tools could enhance their effectiveness by integrating features such as video examples, immersive cultural simulations, or interactions with native speakers to offer learners the context needed to navigate these complexities more effectively.

2. Standardization and Bias Concerns

Another concern is that AI may inadvertently promote language standardization and marginalize dialects and regional language varieties. Crompton (2023) notes that educators need to recognize and validate the linguistic variety that is present among their students because AI tools often solely focus on the widely accepted variety of languages which could result in the neglect of linguistic diversity. Because AI is reliant on data-trained algorithms, less common dialects and non-standard language forms may receive inadequate support and therefore reinforce language standardization.

This issue presents a complex dilemma. Linguistic diversity is integral to group identities, particularly within immigrant communities in the United States. Students who speak non-mainstream languages, especially those with fewer speakers, face a significant disadvantage when it comes to educational resources. For instance, in Omaha, Nebraska, where I live, there are students who speak languages indigenous to Central America at home, such as Nahuatl and K’iche’. Unlike their English- and Spanish-speaking peers, these students often lack the linguistic support needed in school. Without access to resources that honor their home languages, they may feel pressured to adopt a mainstream language for academic success, risking the loss of their native languages.

However, I also see the importance of some level of standardization in language learning, particularly when the goal is effective communication. Not all language varieties are mutually intelligible, and to facilitate broader understanding, speaking a widely recognized dialect has clear benefits. Yet, balancing standardization with the need to preserve linguistic diversity is challenging. While this challenge is not unique to AI, applications in language learning need to be thoughtfully designed to support both effective communication and cultural diversity, ideally providing resources that respect and incorporate various linguistic identities.

3. Ethical Issues

Ethical concerns are also a significant criticism of AI in education, especially regarding potential algorithmic biases. Mariyono (2024) emphasizes that AI algorithms trained on biased data may inadvertently disadvantage certain demographics, resulting in flawed assessments and failure to meet the needs of diverse learner populations. This issue is particularly relevant for language learning in multicultural classrooms, where varied linguistic backgrounds require inclusive teaching methods.

As shown in Fig. 4, the data that is being used to train AI models often contains a significant English and European bias. This bias poses risks in accurately assessing student progress, especially for learners from non-European or non-English speaking backgrounds. The AI models, trained primarily on Western data, may lack sufficient cultural context when generating or evaluating content for these students. The video below also underscores that marginalized groups in the United States—predominantly racial and linguistic minorities—are the most vulnerable to these biases, as they are underrepresented in available data. This raises several essential questions about the inclusiveness of AI-driven education tools and their capacity to support all students equitably.

Fig. 4: DePalma, D. A. (2023). Linguistic Makeup of AI Training Data. tcworld magazine. https://www.tcworld.info/e-magazine/intelligent-information/wrestling-with-the-ethics-of-genai-1285

Video 2: Code.org. (2020). Ethics & AI: Equal Access and Algorithmic Bias [Video]. YouTube. https://www.youtube.com/watch?v=tJQSyzBUAew

Purpose of the Practice

AI-driven language learning tools aim to address several pressing issues in language education. Traditionally, language classes can only provide a limited amount of individualized attention to each student, with teachers balancing the varying skill levels, learning speeds, and individual needs of each student. This challenge is amplified in classrooms where students speak different native languages, as teachers must employ instructional strategies that cater to multiple linguistic backgrounds and skill levels. AI-powered tools are designed to bridge this gap by providing personalized and adaptive learning experiences (Oke et al., 2023). For example, these tools can provide real-time feedback, tailor lessons to individual progress, and recommend practice activities based on specific areas of improvement. As Cope and Kalantzis (2024) note, “As a supplementary teacher of sorts, GenAI may end the quest to find…a method of instruction as effective as one-on-one tutoring.”

Researchers are also interested in exploring whether AI-based learning could foster greater engagement and self-directed learning. For example, Oke et al. (2023) found that when AI is used in collaboration with other learning management systems (LMS), it creates an environment that promotes self-paced learning, making the learning experience interactive and more accessible. Huang et al. (2023) also found that AI can be used to increase learner engagement and therefore motivation. Both of these studies are especially relevant in addressing the motivational challenges that come with self-paced learning, where learners often struggle to maintain a consistent study routine.

Additionally, researchers want to determine if AI can enhance accessibility to language for students with limited resources. By using AI tools that can be accessed via mobile devices, such as Duolingo, Babbel, and other GPTs, educators hope to provide an equitable language-learning experience for students who may not have the means to attend private tutoring or language classes (Patil, 2024). In this way, AI tools hold the potential to democratize language education, allowing anyone with an internet connection to access language learning resources tailored to their level and goals.

Context of the Educational Practice Applications

AI-driven language learning tools have been applied in various contexts, including public K-12 classrooms and individual learning environments. The specific tools explored in this section include Lingvist, a tool that customizes vocabulary practice based on individual learning progress, and Duolingo, an AI-powered app that personalizes language lessons using adaptive algorithms.

Classroom Applications in K-12

In K-12 education, AI-driven learning tools have been introduced to support language education classes. For example, at Desert Ridge High School in Arizona, educators implemented Lingvist as a supplementary learning tool for Spanish and French courses across different proficiency levels (Lingvist, 2020). Lingvist is an app that helps students build their vocabulary in their target language through structured, contextualized practice. The app uses cloze tests, where specific words are removed from sentences, requiring students to use context clues and a provided English translation to fill in the blanks. These words are then reviewed systematically to reinforce retention.

In a video shared by Lingvist, students highlighted the app’s benefits, including its mobile app and the opportunity it gave them to preview vocabulary before class, similar to a flipped classroom model. This approach allows students to engage with new material outside of class, reserving classroom time for teacher-guided activities that reinforce learning. Lingvist’s flexibility allowed students to practice both in school and at home, enabling teachers to reach a diverse group of students with varying proficiency and motivation levels without relying solely on teacher-led instruction.

Video 3: Linvist. (2023). Students review their learning experience with Lingvist [Video]. YouTube. https://www.youtube.com/watch?v=Pyo74BUaEX0

Individual Learning Contexts

Outside of formal educational settings, adults use AI-driven language tools independently to study foreign languages. For instance, apps like Duolingo and Babbel have become popular for individual learners who are looking to learn a new language or improve existing skills at their own pace. Both platforms offer a wide selection of languages, and Duolingo’s premium subscription, “Duolingo MAX,” includes a chatbot feature that simulates real conversations in languages like Spanish, French, and Portuguese through a video call interface. This feature helps learners practice reading, listening, and speaking skills in an interactive manner.

For many users without access to native speakers, such tools provide a valuable opportunity to practice language skills from the comfort of their homes. The adaptive nature of these tools allows users to progress at their own speed and select lessons that align with their interests or goals.

Findings and Outcomes of the Implementation

​The application of AI-driven language tools across these two contexts has yielded several significant outcomes.

1. Increased Engagement and Motivation

One of the most prominent findings is that AI-powered language learning tools have had a positive impact on student motivation and engagement (Huang et al., 2023). The gamified format of Duolingo encourages students to participate consistently, with features like streaks and level achievements creating a sense of accomplishment (Huynh et al., 2016). This gamification has been shown to be particularly effective in sustaining engagement among younger learners who may otherwise struggle with language learning due to attention and motivation challenges (Foutoulakis, 2024).

2. Enhanced Personalization and Progress

The adaptive capabilities of AI tools such as Lingvist have allowed students at the university level to experience a more personalized form of vocabulary learning. By customizing vocabulary experiences based on individual performance, Lingvist enables students to progress at their own pace, minimizing the frustration associated with encountering content that is either too easy or too difficult. Research by Pin-Chuan Lin and Chang (2023) shows that such personalization helps to enhance learning outcomes by providing an ideal balance between challenge and support. This approach aligns with Vygotsky’s Zone of Proximal Development (ZPD) (Fig. 5), where learners achieve better results when they operate within a level slightly above their current abilities, supported by AI’s real-time adjustments.

Fig. 5: University of Buffalo. (n.d.). Zone of Proximal Development. https://www.buffalo.edu/catt/teach/develop/build/scaffolding.html

3. Equity and Accessibility Considerations

The implementation of AI-driven language learning tools also raised important questions regarding equity and accessibility. While these tools offer access to language learning for students outside of the classroom, they rely heavily on internet connectivity and compatible devices. In resource-limited settings, such as certain schools in low-income areas, this dependence on technology poses a barrier. In the school that I work in, not every student has consistent access to devices or the internet outside of school, which limits the utility of these apps for those users. Rusmiyanto et al. (2023) highlight that while AI can democratize access to education, it also risks widening existing digital divides if not properly supported by infrastructure and resources. 

Educator Training

While AI tools like Duolingo provide adaptive learning, educators must receive adequate training to integrate these tools effectively into their teaching. Professional development to help teachers understand how to balance AI-driven learning with traditional methods can contribute to more evidence-based teaching practices and the development of teacher skill sets (Tammets & Ley, 2023).

Connecting Practice to Theory

Vygotsky’s Sociocultural theory is partially fulfilled by AI-driven language tools that offer interactive, personalized learning experiences, and mimic elements of social interaction through features like adaptive chatbots. For instance, the video call feature in Duolingo can simulate conversational practice, a form of scaffolding that aligns with the ZPD by providing learners with language input that is slightly beyond their current abilities (Pin-Chuan Lin & Chang, 2023). Similarly, Krashen’s Input Hypothesis, which emphasizes the need for comprehensible input at a level just above the learner’s current level, is realized through AI’s ability to adapt to individual users, creating a tailored learning progression that aligns with this theoretical approach.

Constructivist theories, which advocate for learner-centered, self-directed exploration, are also supported by AI applications that foster autonomy, allowing users to engage with content at their own pace. By providing real-time feedback and performance tracking, AI promotes self-directed learning and allows for the greatest independence—elements that constructivism supports for effective language acquisition. However, while AI-driven platforms fulfill certain aspects of these theories, they fall short in delivering the full scope of interactive, culturally sensitive communication necessary for language mastery.

Limitations and Unrealized Potentials

Despite their strengths, AI language tools face limitations, particularly in fostering genuine social interaction and cultural context, both of which are vital to the language acquisition process. Some systems are beginning to address this by incorporating a broader range of dialects, regional variations, and cultural norms in their datasets. For instance, tools like Google Translate have incorporated a wider selection of languages and dialects, allowing for more accurate and culturally relevant translations (Medvedev, 2016). However, true cultural sensitivity requires more than linguistic data; it requires AI systems to be able to respond to the distinctions of pragmatic language use, regional customs, and socio-cultural contexts that cannot be captured by large datasets alone.

To address these gaps, emerging approaches could integrate AI tools with cultural immersion experiences. For example, AI-powered platforms might collaborate with virtual reality (VR) technology to create environments where learners engage in culturally specific scenarios, such as navigating a local market or attending a cultural festival. These environments could combine the language scaffolding provided by AI with the sensory and contextual depth of VR, offering learners a richer understanding of cultural nuances.

Additionally, concerns about the ethical dimensions of AI language learning tools have emerged, particularly around issues of standardization and bias. Current AI systems often perpetuate linguistic biases by focusing primarily on majority languages and standard dialects, which marginalizes linguistic minorities. Developing AI capable of learning from diverse linguistic bodies and integrating cultural and regional variations could significantly enhance the inclusivity and authenticity of AI-driven language learning.

While these innovations hold promise, they also require significant technological investment, reliable internet access, and a willingness to collaborate across cultural boundaries. Despite these hurdles, such strategies offer a pathway to overcoming some of AI’s limitations and emphasize the need for AI to be a supplementary tool rather than a standalone solution for language learning.

Effectiveness Assessment of AI in Language Education

To gauge the success of AI-driven language learning tools, it is crucial to adopt methodologies to assess their impact on language acquisition outcomes. Recent studies have explored how AI tools affect learner satisfaction, engagement, and proficiency, but there remains a gap in longitudinal assessments that measure their effectiveness over time. For instance, research could track learner progression in terms of fluency, vocabulary retention, and pragmatic language use, comparing AI-enhanced learning with traditional classroom methods. Additionally, user experience surveys could address learner satisfaction and engagement levels with the AI systems. Studies like those by Lin & Chang (2023) and Rusmiyanto et al. (2023) have provided valuable insights, but further research into AI’s long-term impact on linguistic proficiency and cultural competency is needed. By integrating these assessment practices, educators and developers can refine AI tools to meet the diverse needs of language learners more effectively.

Recommendations and Future Research

To maximize the potential of AI-driven language learning tools, future research, and development should focus on bridging the current gaps in cultural and social dimensions of language acquisition. I believe that several areas are worth exploring to advance the field:

1. Integrating Cultural and Pragmatic Language Learning

Future research should explore how AI can better replicate authentic social interactions, incorporating cultural norms, idiomatic expressions, and practical language use. For instance, AI tools could use context-specific learning modules that adapt to regional linguistic distinctions and cultural practices, enhancing the learner’s ability to navigate real-world conversations. While this approach requires significant investment in time and resources, it would contribute to creating systems that promote and preserve linguistic diversity while helping learners become more comprehensive speakers of their target language.

2. Fostering Interdisciplinary Collaboration

The development of culturally sensitive and linguistically diverse AI tools will require collaboration between linguists, educators, sociologists, and AI developers. Interdisciplinary teams could design tools that balance technological innovation with pedagogical principles to ensure that AI supports both linguistic competence and cultural literacy. Developers could benefit from the insights into sociolinguistic diversity and pragmatic language use to create more culturally sensitive AI systems. Additionally, language programs could implement AI as a supplementary tool to blend AI-driven exercises with teacher-led instruction that focuses on cultural and conversational skills. This dual approach allows AI to excel in providing structured, repetitive practice, while teachers guide learners through the nuances of language.

3. Encouraging Community Involvement

Partnering with cultural organizations and native speakers could enrich AI tools with localized knowledge, making them more authentic and relevant. This approach could also involve crowdsourcing regional and dialect-specific content to enhance the AI’s linguistic repertoire. By incorporating the insights of local communities, developers can ensure that AI tools reflect the cultures of the languages they aim to teach.

By pursuing these directions, researchers and developers can create AI-driven tools that not only address the technical challenges of language acquisition but also provide learners with the cultural and social competencies needed for authentic communication. Future work in this field should aim to integrate AI’s strengths in efficiency and scalability with the human-centered elements that define meaningful language use.

In summary, AI in language learning presents a promising yet complex future. While AI-driven tools align with established theories of language acquisition, they face significant challenges in the delivery of the full depth of language’s social and cultural dimensions. Blending AI’s strengths with human-led instruction may be the most effective approach, enabling learners to benefit from technology’s efficiency while gaining the cultural and interactive experience necessary for true language proficiency.