Pedagogical AI-assisted Tool in Learning English Nouns in Some Selected Secondary Schools, Gwale Local Government Area of Kano State.

Cite this article as: Muhammad, B. Y., & Rasheed, A. K. (2026). Pedagogical AI-assisted Tool in Learning English Nouns in Some Selected Secondary Schools, Gwale Local Government Area of Kano State. Sokoto Journal of Linguistics and Communication Studies (SOJOLICS), 2(1), 21-33. https://doi.org/10.36349/sojolics.2026.v02i01.003

By

Baba Yusuf Muhammad
School of Cont. Edu.
Bayero University, Kano

&

Anas Kabir Rasheed
School of Cont. Edu.
Bayero University, Kano
08031520734
anasrasheedmakarfi@gmail.com

Abstract

The paper aims to demarcate the impact and take exception of AI-Assisted Cognitive aspect as a pedagogical tool for teaching English noun aspect in the context of Nigerian ESL classroom. Specifically, the study proposes to investigate whether AI-integrated training may boost learners’ knowledge and engagement compared to typical grammar teaching methodologies. The research was prompted by the rising usage of AI in education and the need to address the limits of conventional grammar instruction in Nigerian ESL situations. A four-week quasi-experimental design was created, enrolling Senior Secondary School students from four selected secondary schools in Gwale local government. The students were categorized into an experimental group (n=60), which received instruction using AI-assisted cognitive grammar, and a control group (n=61), which was taught using traditional methodologies. The study was anchored by Krashen’s Input Hypothesis, Cognitive Grammar Theory, and the TPACK framework. Data were acquired using pre-tests, post-tests, and perception-based questionnaires, then analyzed using SPSS utilising independent and paired t-tests. Findings indicated a statistically significant improvement in the post-test scores of the experimental group (p < 0.05), suggesting the efficacy of AI-assisted cognitive grammar in enhancing students’ understanding of noun classes. Additionally, questionnaire responses demonstrated enhanced confidence, interest, and participation among students in the experimental group. The study reveals that AI-supported training presents a possible alternative to current strategies in ESL grammar instruction, promoting both cognitive improvement and student motivation.

Keywords: Artificial Intelligence (AI) in Education, AI-Assisted Grammar Instruction, English as a Second Language (ESL), Grammar Pedagogy

1.0 Introduction

Artificial intelligence (AI) has taken the world by storm, thereby transforming everything with conventional teaching and learning methodologies in education not left behind Artificial intelligence (AI) has taken the world by storm, and its transformative influence extends to education, impacting even conventional teaching and learning methodologies (UNESCO, 2023; Wei, 2023). AI-powered technologies, such as ChatGPT, Claude, MetaAI, Grok etc and other Natural Language Processing (NLP) applications, have revolutionised language learning via instant feedback, personalized teaching, and interactive learning experiences (Navaie, Panwar, & Asino, 2024; Chen et al., 2021). By providing dynamic, context-sensitive learning methodologies, AI has effectively addressed the limitations of conventional rote memorization in vocabulary acquisition and grammar instruction Artificial intelligence has successfully mitigated the limitations of conventional rote memorization in vocabulary acquisition and grammar instruction by providing dynamic, context-sensitive learning methodologies (Sanni & Yusuf, 2022; Bećirović et al., 2021). This alteration has significantly as cited by Rogan & San Miguel, 2013; Adeyanju et al., 2024 has impacted English grammar training, since as AI enhances comprehension via through adaptive coaching, automated assessments, and real-time illustrations (Rogan & San Miguel, 2013; Adeyanju et al., 2024).

A key area of English grammar that particularly benefits from AI integration is the study of word classes the fundamental building blocks of language, including nouns, verbs, adjectives, and adverbs. Ndubueze et al. (2015) argue that mastery of word classes is crucial for ESL learners, directly impacting their overall language proficiency, sentence construction, and comprehension. At the core of this word class system lie noun classes, which refer to the categorization of nouns based on certain features like gender, animacy, countability, and grammatical function (Teshaboyeva & Rasulova, 2024). Understanding the nuances of noun classes is therefore essential for ESL students as they develop a deeper grasp of the English language (Mamadaliyeva & Sabrina, 2025). One may argue that the core area of English grammar that benefits most from AI integration is the study of word classes, which still include the fundamental building blocks of language: nouns, verbs, adjectives, and adverbs. According to Ndubueze et al., (2015), the success in word classes is crucial for ESL (English as a Second Language) learners since it has a direct influence on their overall language proficiency, sentence construction, and meaning. In the past, rule-based learning, known for its rigidity and detachment from practical applications, which may be rigid and detached from actual applications—has been often applied in word class lessons. When paired with Cognitive Grammar (CG), AI-assisted education offers an alternate method that prioritizes extensive knowledge and deep understanding over thoughtless memorization. By claiming that grammar is a system built on human thought and conceptualization rather than just a collection of abstract rules, Langacker's (1987, 1991) introduction of Cognitive Grammar questions the standard rule-based method. This method is in line with educational grammar, which tries to teach grammar as a set of useful and understandable ideas as opposed to a strict set of rules.

In contrast, the introduction of Cognitive Grammar by Langacker in 1987 and 1991 challenges the conventional rule-based approach, highlighting grammar as a reflection of human thought processes and conceptualizations, rather than a set of abstract rules. Cognitive Grammar (Langacker, 1987, 2008) posits that grammatical categories and patterns emerge organically from the way we conceptualize and interact with the world around us. In the context of word class lessons, the integration of Cognitive Grammar (CG) with AI provides a personalized approach, shifting the focus from rote learning to meaningful comprehension and extensive knowledge acquisition. When paired with artificial intelligence (AI), cognitive grammar helps students better grasp complex language patterns by giving real-time, contextualised explanations that are changed to each learner's requirements. (Wei, 2023; Navaie, Panwar, & Asino, 2024).

This study tends to explores the impact of how ESL students grasp English word classes better after getting AI-assisted Cognitive Grammar training on ESL students' understanding of English noun classes. Additionally, it examines the potential of AI to improve this research also analyses how artificial intelligence (AI) may increase grammar knowledge, vocabulary acquisition, and overall language competence among ESL students in Nigeria by comparing students taught using AI-assisted It compares the performance of students who receive AI-assisted cognitive grammar instruction with those taught using traditional teaching methods. of learning in Nigeria. The findings are intended to contribute to the developing body of information concerning the interaction of this research are expected to enrich the existing knowledge on the interplay between pedagogical grammar, AI, and ESL education in Nigeria.

2.0 Literature review

Artificial Intelligence in Language Education

The integration of Artificial Intelligence into language education is not a recent phenomenon; it builds on decades of work in Intelligent Computer-Assisted Language Learning (ICALL), which sought to combine natural language processing with pedagogical principles to support second language acquisition (Chapelle, 2001; Godwin-Jones, 2022). Early ICALL systems were largely rule-based and limited in their capacity to handle the variability of learner language, but the emergence of large language models has considerably expanded what AI tools can offer, including context-sensitive explanations, adaptive practice, and conversational feedback (Godwin-Jones, 2022). Warschauer and Healey (1998) had earlier argued that the value of any computer-assisted language learning tool lies not in the technology itself but in how it is pedagogically situated, a caution that remains relevant to current AI-assisted grammar instruction.

AI and Grammar Instruction: Automated Feedback and ICALL Tools

A substantial strand of ICALL research has focused on automated written corrective feedback (AWCF) and its effect on learners' grammatical accuracy. Heift and Schulze (2007) demonstrated that intelligent tutoring systems capable of parsing learner errors and generating targeted feedback could support noticing and uptake more effectively than generic correction.

Similarly, Nagata (1993, 2009) found that computer-generated metalinguistic feedback often outperformed simple error flagging in helping learners internalise grammatical rules, particularly when feedback was tied to explanations rather than mere correction. Ellis's (2009) typology of corrective feedback further underscores that the effectiveness of AI-generated feedback depends on its timing, explicitness, and alignment with learners' developmental readiness, a principle increasingly echoed in evaluations of generative AI tools such as ChatGPT and Grammarly for grammar instruction (Godwin-Jones, 2022).

Cognitive Grammar as a Pedagogical Framework

Cognitive Grammar (Langacker, 1987, 1991, 2008) reconceptualises grammatical structure as an outgrowth of general cognitive processes such as categorisation, construal, and conceptualisation, rather than as an autonomous formal system. This reframing has proven attractive to language pedagogy because it allows abstract categories, including noun classes, to be taught as meaningful and motivated rather than arbitrary (Tyler, 2012). Within a pedagogical grammar tradition, Cognitive Grammar has been used to help learners visualise how features such as countability, animacy, and boundedness shape noun behaviour, offering an explanatory alternative to rule memorisation (Achard & Niemeier, 2004). Scholars applying this approach report that learners taught through conceptually grounded explanations tend to retain grammatical distinctions longer than those taught through decontextualised drills (Tyler, 2012).

Integrating AI with Cognitive Grammar in Noun Class Instruction

Relatively few studies have examined the deliberate pairing of AI tools with Cognitive Grammar principles, though the rationale for doing so is increasingly recognised. Because generative AI systems can produce learner-specific, context-rich explanations on demand, they are well positioned to operationalise Cognitive Grammar's emphasis on usage-based, meaning-oriented instruction (Wei, 2023; Navaie, Panwar, & Asino, 2024). Empirical support for this synergy comes indirectly from studies such as Behforouz and Al Ghaithi (2024) and Chen and Lin (2023), both of which found that AI-mediated grammar practice produced stronger learning outcomes than conventional instruction, even though neither study explicitly framed its intervention through Cognitive Grammar. For noun classes specifically, Ndubueze et al. (2015) and Teshaboyeva and Rasulova (2024) confirm that ESL learners struggle most where instruction fails to make the semantic basis of noun categorisation explicit, which is precisely the gap that an AI-supported Cognitive Grammar approach is positioned to close.

AI in ESL Education: The Nigerian and Wider African Context

Studies situating AI-assisted grammar instruction within Nigerian or broader African ESL contexts remain scarce. Sanni and Yusuf (2022) and Adeyanju et al. (2024) note that while AI adoption in Nigerian education is growing in discourse, actual classroom-level integration is constrained by infrastructural limitations, inconsistent internet access, and limited teacher training in AI literacy. Where studies do exist, they tend to focus on tertiary-level academic writing support rather than secondary school grammar pedagogy (Al-Matari, 2023), leaving the effectiveness of AI-assisted cognitive grammar instruction for Nigerian secondary school ESL learners largely untested.

Critical Perspectives and the Research Gap

Alongside optimism about AI's pedagogical potential, researchers caution against over-reliance on AI tools without adequate teacher mediation. Holmes et al. (2022) and Cakmak (2022) warn that unsupervised use of AI in classrooms can encourage superficial engagement, plagiarism, and diminished critical thinking if not paired with structured pedagogical design. Okafor and Williams (2023) and Bećirović et al. (2021) similarly argue that AI should complement, rather than substitute, teacher expertise, particularly in contexts where learners have limited prior exposure to independent digital learning. Taken together, the literature suggests that AI holds considerable promise for grammar instruction when grounded in a coherent pedagogical framework such as Cognitive Grammar, yet empirical evidence from Nigerian secondary school contexts is largely absent. This study addresses that gap by examining whether AI-assisted Cognitive Grammar instruction improves Nigerian ESL learners' understanding of English noun classes relative to traditional methods.

3.0 Theoretical Framework

AI’s presence in education has transformed traditional teaching methods, creating personalized, flexible, and engaging learning experiences (Yang & Kyun, 2022). Researchers (Jawaid et al., 2025; Yeh,2025; Chen et al., 2025; Mohebbi, 2025; Mushthoza et al., 2023; Yeh, 2024) report that AI has redefined language teaching and learning, creating a dynamic shift from static, classroom-centric models to flexible, learner-driven experiences. Moreover, AI has impacted both students and teachers and education policymakers with AI tools improving by offering tools that improve learning outcomes and streamlining administrative processes like grading, feedback, research content generation and plagiarism detection (Yuan & Kyun, 2022). Dakakni & Safa (2023) describe AI as software systems designed to mimic a range of human cognitive functions, such as deciding, understanding, analyzing, self-correcting and remembering. AI focuses on creating detailed machine-based simulations of human intelligence through the development of software algorithms that can also interact with the environment. Pettela (2020) defines AI as machines that perform cognitive tasks related with human intellect, it has become crucial in education and other disciplines by improving administrative chores, pedagogy, and customized learning experiences. The AI-human relationship is crucial, as AI systems depend on human-defined objectives and design (Holmes et al., 2022).

A fundamental principle in AI-driven language education is the interdependence between humans and technology. AI does not function autonomously; rather, it operates within frameworks designed, programmed, and refined by humans. This symbiotic relationship ensures that AI tools enhance—rather than replace—human expertise in teaching and learning (Holmes et al., 2022; Demirel, Lusta & Göksu, 2025).

The integration of AI in English education, particularly through tools like ChatGPT, Claude and Gemini, has revolutionized vocabulary learning by providing adaptive learning environments and real-time personalized feedback (Fitzpatrick, 2023). Studies show AI’s positive effect on learning outcomes, particularly in language teaching and learning (Thenmozhi et al., 2023; Mishra & Kumar, 2020 ).

Research Questions:

The research is also guided by the questions below: 

1. Is there a significant difference between the pre-test and post-test results of students in the experimental group and those in the control group?

2. Is there a significant difference between the post-test results of students in the experimental group and those in the control group?

3. What are the perceptions of Nigerian ESL learners in the experimental group regarding the effectiveness of AI-assisted cognitive grammar as a tool for learning English noun classes, compared to the control group’s perceptions of traditional teaching methods?

Research Objectives:

This study’s aim is to investigate the efficacy of using cognitive grammar as a pedagogical approach, braced by AI tools, in enhancing the learning of English noun classes among Nigerian ESL learners at some Secondary Schools in Gwale LG. The objectives of the research are:

1. To assess the impact of cognitive grammar on students’ understanding and use of English noun classes.

2. To compare the performance of students taught using cognitive grammar with those taught using traditional grammar methods.

3. To evaluate the role of AI tools in improving students’ comprehension of noun classes.

4.0 Methodology

The research adopted a mixed-method technique of research which included experimental and qualitative techniques. A total number of 121 students from the selected schools were used, aged 13 to 17, who are divided into A, B, C and D classes. They are familiar with Meta AI or ChatGPT but lack prompting skills. The two teaching approaches traditional grammar (classes A and B) with only a whiteboard, marker, and textbook, and cognitive grammar (classes C and D) leveraging AI technology (via students' cellphones and the school’s desktop computer) are juxtaposed. Cognitive grammar training is supported by grammar-checking software and natural language processing approaches. AI tests and assignments were used to obtain data. The performance of the cognitive and conventional grammar groups will be compared using SPSS's t-test technique in order to find if the AI-assisted strategy delivers statistically significant gains. The study tries to determine the degree to which AI-driven cognitive grammar promotes grammatical correctness and comprehension.

Intervention: Four Weeks of Pre-test Post-test Examining the Four-Week Test Process

Over four weeks, the experimental and control groups engaged in various instructional strategies to acquire English word courses. The procedure is broken out weekly here:

First Week: Pre-Test and Basic Lessons

i. To gauge their baseline knowledge of English word classes, both groups took pre-tests.

ii. The control group received instruction in a conventional manner—whiteboard, marker, and the assigned English textbook (Intensive English).

 

Week 2: Continued Lessons and AI Practice

i. The control group proceeded with teacher-centered education, concentrating on rote memory and textbook activities.

ii. The experimental group participated in interactive AI tasks, where they utilised ChatGPT for grammatical explanations, real-time feedback, and practice with sentence structures.

iii. Discussions on how meaning connects to language were stressed in the experimental group, making the learning more conceptual rather than rule-based.

Week 3: Advanced Lessons and AI Integration

iv. The control group proceeded with typical teaching approaches, including class activities and written assignments.

v. The experimental group got more tailored AI input, with AI assessing their grammar use and offering changes.

vi. Students in the experimental group demonstrated higher interest, asking more complicated grammar-related questions and investigating alternative AI prompts.

Week 4: Final Review and Post-Test

vii. A post-test was administered for both groups to assess learning improvement.

viii. The control group showed only slight improvement, as their learning relied on memorization without interactive reinforcement.

ix. The experimental group demonstrated significant improvement, as they had multiple opportunities to practice, receive AI feedback, and understand the deeper connections between grammar and meaning.

A questionnaire was also presented to the experimental group to get their perspectives on employing AI for learning.

5.0 Data Analysis

The results of the study were analyzed using a t-test to compare the pre-test and post-test scores of both the experimental group, which received instruction using cognitive grammar with AI assistance, and the control group, which was taught using traditional grammar instruction. This statistical test was chosen to determine whether there were significant differences in the mean scores between the two groups before and after the intervention. The analysis involved calculating the mean scores, standard deviations, and t-values for each group's pre-test and post-test results. The findings are summarized in the next section.

Results 

Tests 

The results of the study are presented using a t-test analysis to compare the pre-test and post-test scores of both the experimental group (taught using cognitive grammar with AI assistance) and the control group (taught using traditional grammar instruction).

Table 1: Sample Size

Group Boys Girls Total

Experimental Group 30 30 60

Control Group 30 31 61

Total 60 61 121

The above table shows how the study involved a total of 121 SS3 students divided into two groups: 60 in the experimental group and 61 in the control group. Each group was mixed, consisting of both boys and girls, with the experimental group having 30 boys and 30 girls, while the control group had 30 boys and 31 girls, with both from the same school for easier assessing, monitoring and observation. This mixed-gender composition allows for gender-inclusive analysis without the need to separate the data by sex.

Table 2: Comparison of Pre-Test and Post-Test Scores of the Experimental Group and the Control Group

Group N Mean

(Pre-Test Score) SD

(Pre-Test) Mean

(Post-Test Score) SD

(Post-Test) t-value p-value

Experimental Group 60 45.3 5.8 78.6 6.2 12.35 <0.001

Control Group 61 44.7 6.1 49.5 6.5 1.95 0.056

While the control group only showed a little improvement, the experimental group demonstrated a notable development with their post-test score shooting from 45.3 to 78.6. While the p-value of <0.001 verifies the significance, the t-value of 12.35 for the experimental group suggests a large statistical difference. By comparison, the control group's t-value of 1.95 and p-value of 0.056 point to no significant change. This emphasizes how well AI-assisted Cognitive Grammar helps students do better in studying English noun classes.

Table 3: Comparison of Post-Test Scores Between Experimental and Control Groups

Group N Mean

(Pre-Test Score) SD

 (Pre-Test) Mean

(Post-Test Score) SD

(Post-Test) t-value p-value

Experimental Group 60 45.3 5.8 78.6 6.2 11.25 <0.001

Control Group 61 44.7 6.1 49.5 6.5 1.56 0.12

‘The post-test outcomes of the experimental and control groups reveal a significant difference. With a post-test mean score of 78.6, the experimental group much surpassed the control group, which scored just 49.5. The t-value of 11.25 for the experimental group and p-value of less than 0.001 verify a significant effect of the AI-assisted cognitive grammar intervention. The t-value of 1.56 and p-value of 0.12 for the control group highlight even more the little variation in their findings. This demonstrates how AI-assisted learning may help language learning more than conventional approaches.

Table 4: Comparison of Mean Score Gains Between Experimental and Control Groups

Group N Mean Pre-Test Score Mean Post-Test Score Mean Gain Score SD (Gain) t-value p-value

Experimental Group 60 45.3 78.6 33.3 5.4 13.87 <0.001

Control Group 61 44.7 49.5 4.8 6.2 1.62 0.11

The post-test results of the experimental and control groups demonstrate a dramatic improvement. The experimental group improved by an average of 33.3 points, compared to only 4.8 points in the control group. The t-value of 13.87 and the p-value of <0.001 for the experimental group demonstrate that the gain is statistically significant. Meanwhile, the control group’s t-value of 1.62 and p-value of 0.11 reveal no significant improvement. This clearly demonstrates that AI-assisted cognitive grammar considerably boosts learners’ grasp of noun classes, much more than conventional grammar training.

5.1 Discussion of Results

This research analyses the potential benefits of AI-assisted cognitive grammar in English noun education for Nigerian ESL learners. The study focusses at the students' views towards AI-assisted learning and compares the pre- and post-test outcomes of the experimental and control groups. The experimental group's scores considerably rose (from 45.3 to 78.6) when utilising AI tools, corroborating prior findings that AI increases grammar learning via personalised feedback and interactive experiences (Pettela, 2020; Behforouz & Al Ghaithi, 2024) UNESCO, 2021). The most successful language learning happens when children interact with understandable material that is just a little bit above their level of competency, according to Krashen's information Hypothesis. This idea is backed by these facts. Several factors may account for these dramatic results. First, the AI system likely provided immediate, personalized feedback - a critical advantage over conventional instruction where teacher attention is necessarily divided. Second, the interactive nature of AI tools probably increased student engagement and motivation, as suggested by the experimental group's enthusiastic interview responses.

Third, the cognitive grammar approach, when delivered through adaptive AI, may have helped students develop deeper conceptual understanding rather than superficial rule memorization. However, the control group, which got conventional grammar teaching, only barely improved (from 44.7 to 49.5), confirming the assumption that traditional approaches do not offer the interaction and engagement essential for language acquisition (Godwin-Jones, 2022). The experimental group's favorable ratings (90%) of AI's efficacy coincide with research indicating the promise of personalized and engaging AI technologies (Yuan & Kyun, 2022; Rusmiyanto et al., 2023). However, as students believed that traditional education was less engaging and did not give customized feedback, the control group raised worries about its limits (Zhang & Cao, 2022). The results offer credibility to the TPACK framework (Mishra & Koehler, 2006), Krashen's Input Hypothesis, and Cognitive Grammar Theory (Langacker, 1987–1991) by underlining the importance of merging AI, pedagogy, and subject knowledge to increase learning. Despite fears that technology may dehumanize education, this research supports the premise that AI should supplement existing teaching techniques rather than replace them (Holmes et al., 2022). (Jones and Godwin, 2022).

In conclusion, AI-assisted cognitive grammar is a more effective teaching technique than conventional methods, boosting students' knowledge of English noun courses. As AI technology progresses, it will play a vital role in delivering customized, engaging, and meaningful learning experiences. To increase the quality of education overall, they need to be coupled with conventional teaching approaches. Future studies must look at AI's wider usage in ESL instruction.

6.0 Conclusion

This research reveals that AI-assisted cognitive grammar greatly enhances students’ understanding of English noun classes compared to conventional techniques. The experimental group’s superior post-test performance and improved learner confidence illustrate the benefits of using AI technology in ESL education. Despite early obstacles, AI-driven learning shown to be intriguing and productive. The results imply that instructors should include AI into their classes to boost engagement, give focused feedback, and encourage customised learning. Future research and teacher training should concentrate on utilising AI’s ability to design adaptive and rewarding ESL learning environments. Future research should investigate: (1) long-term retention of AI-acquired knowledge, (2) transferability to other language skills, and (3) optimal implementation models (blended vs. standalone). Qualitative studies could illuminate the specific mechanisms through which AI tools facilitate superior learning outcomes.

References

AbuSahyon, A. S. A. E., Alzyoud, A., Alshorman, O., & Al-Absi, B. (2023). AI-driven technology and Chatbots as tools for enhancing English language learning in the context of second language acquisition: a review study. International Journal of Membrane Science and Technology, 10(1), 1209-1223.

Adeyanju, A. 1., Oderinde, K.R., Titilayo, A.O., Ajoke, G.O., Oyeladun, M.B., Samuel, F.A. (2024). Artificial Intelligence-Based Essay Grading System. Engineering and Technology Journal, 9(7), 4382-4388.

Adeyemi, J., & Okonkwo, P. (2023). The Effectiveness of AI-Assisted Grammar Instruction in Nigerian ESL Classrooms. Journal of African Linguistics and Education, 15(2), 120-138.

Ali, Z. (2020). Artificial Intelligence (AI): A Review of Its Uses in Language Teaching and Learning. In IOP Conference Series: Materials Science and Engineering, 769(1), 012043. IOP Publishing.

Al-Matari, A. (2023, September). Artificial Intelligence and the Future of Teaching and Learning. In Pelita Internasional Conference [PIC] (Vol. 10).

Annamalai, N., Eltahir, M. E., Zyoud, S. H., Soundrarajan, D., Zakarnah, B., & Al Salhi, N. R. (2023). Exploring English Language via Chatbot: A Case Study from a Self-Determination Theory Perspective. Computers and Education. https://doi.org/10.1016/j.caeai.2023.100148

Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? The Future of Artificial Intelligence in Schools and Colleges. Nesta Research Report, 1-35.

Bećirović, S., Brdarević-Čeljo, A., & Delić, H. (2021). The Use of Digital Technology in Foreign Language Learning. SN social sciences, 1(10), 246.

Behforouz, B., & Al Ghaithi, A. (2024). Grammar gains: Transforming EFL learning with

 ChatGPT. Educational Process: International Journal, 13(4): 25-41.

Cakmak, F. (2022). Chatbot-Human Interaction and Its Effect on EFL Students’ L2 Speaking Performance and Speaking Anxiety. Noritas-ROYAL (Research on Youth and Language, 16(2), 113-131.

Chen, L., Chen, P., & Lin, Z. (2021). Artificial Intelligence in Education: A Review. IEEE Access, 9, 110685-110701. https://doi.org/10.1109/ACCESS.2021.3104467

Chen, A., Zhang, Y., Jia, J., Liang, M., Cha, Y., & Lim, C. P. (2025). A systematic review and meta‐analysis of AI‐enabled assessment in language learning: Design, implementation, and effectiveness. Journal of Computer Assisted Learning, 41(1), e13064.

Chen, Y., & Choi, Y. (2021). Incorporating AI into English Vocabulary Learning: A Review of Current Practices and Future Directions. Journal of Educational Technology & Society, 24(1), 184-197.

Chen, M. R. A., & Lin, Y. H. (2023). AI chatbot-supported grammar tense learning on EFL students learning effectiveness. In INTED2023 proceedings (pp. 1207-1216). IATED.

Clark, H. (2023). The AI-Infused Classroom: Inspiring Ideas to Shift Teaching and Maximize Meaningful Learning in the World of AI. Elevate Books Edu.

Dakakni, D., & Safa, N. (2023). Artificial intelligence in the L2 classroom: Implications and challenges on ethics and equity in higher education: A 21st century Pandora's box. Computers and Education: Artificial Intelligence, 5, 100179.

Demirel, Ö., Lusta, A., & Göksu, H. (2025). Developing writing skill through artificial intelligence. Pegem Academy Publishing: Ankara

Dinachandra, S. C. (2024). Using Artificial Intelligence (AI) in English Vocabulary Development for Higher Secondary School Students. Research Square, Arunachal University of Studies. https://orcid.org/0000-0002-7447-1214

Eze, C., & Musa, R. (2021). Integrating Artificial Intelligence into the Nigerian Secondary School Curriculum: Challenges and Prospects. West African Journal of Educational Technology, 9(3), 89-104.

Fitria, T. N. (2021). The Use of Technology Based on Artificial Intelligence in English Teaching and Learning. ELT Echo: The Journal of English Language Teaching in Foreign Language Context, 6(2), 213-223.

Fitzpatrick, D., Fox, A., & Weinstein, B. (2023). The Ultimate Guide to Artificial Intelligence in Education. TeacherGoals Publishing.

Godwin-Jones, R. (2022). Partnering with AI: Intelligent Writing Assistance and Instructed Language Learning. Language Learning and Technology, 26(2), 5-24. http://doi.org/10125/73474

Jawaid, A., Batool, M., Arshad, W., ul Haq, M. I., Kaur, P., & Sanaullah, S. (2025). AI and English Language Learning Outcomes. Contemporary Journal of Social Science Review, 3(1), 927-935.

Johnson, M. (1987). The Body in the Mind: The Body in the Mind: The bodily basis of meaning, reason and imagination. Chicago: University of Chicago Press.

Hidayat-ur-Rehman, I. (2024). Examining AI competence, chatbot use and perceived autonomy as drivers of students' engagement in informal digital learning. Journal of Research in Innovative Teaching & Learning, 17(2), 196-212.

Holmes, W., Persson, J., Chounta, I. A., Wasson, B., & Dimitrova, V. (2022). Artificial Intelligence and Education: A Critical View Through the Lens of Human Rights, Democracy, and the Rule of Law. Council of Europe.

Huang, W., Hew, K. F., & Fryer, L. K. (2022). Chatbots for language learning—Are they really useful? A systematic review of chatbot‐supported language learning. Journal of computer assisted learning, 38(1), 237-257.

Khalil, M., Amreen, S., & Ather, F. (2025). Exploring The Role of Ai Chatbots in Enhancing English Grammar and Vocabulary Acquisition: A Study of Learning Dynamics, User Engagement, and Linguistic Development. Journal of Applied Linguistics and Tesol (Jalt), 8(1), 801-812.

Krashen, S. (1982). Principles and Practice in Second Language Acquisition. Pergamon Press.

Krashen, S. (1985). The Input Hypothesis: Issues and Implications. Longman.

Krashen, S. (1989). We Acquire Vocabulary and Spelling by Reading: Additional Evidence for the Input Hypothesis. The Modern Language Journal, 73(4), 440-464.

Langacker, R. W. (1987). Foundations of Cognitive Grammar: Volume I, Theoretical Prerequisites. Stanford University Press.

Langacker, R. W. (1991). Foundations of Cognitive Grammar: Volume II, Descriptive Application. Stanford University Press.

Langacker, R. W. (2008). Cognitive Grammar: A Basic Introduction. Oxford University Press.

Langacker, R. W. (2009). Investigations in Cognitive Grammar. Mouton de Gruyter.

Langacker, R. W. (2013). Essentials of Cognitive Grammar. Oxford University Press.

Mamadaliyeva, Z., & Sabrina, Y. (2025). The Grammatical Categories of Nouns. Innovative Developments and Research in Education, 4(37), 56-58.

Mishra, B. K., & Kumar, R. (2020). Natural Language Processing in Artificial Intelligence. Apple Academic Press.

Mishra, P., & Koehler, M. J. (2003). Not "What" but "How": Becoming Design-Wise About Educational Technology. In Y. Zhao (Ed.), What Teachers Should Know About Technology: Perspectives and Practices (pp. 99-122). Information Age Publishing.

Mishra, P., & Koehler, M. J. (in press). Designing Learning from Day One: A First-Day Activity to Foster Design Thinking About Educational Technology. Teachers College Record.

Mohebbi, A. (2025). Enabling learner independence and self-regulation in language education using AI tools: a systematic review. Cogent Education, 12(1), 2433814.

Mushthoza, D. A., Syariatin, N., Tahalele, O., Telussa, S. I., Rasmita, R., & Mokodenseho, S. (2023). Analyzing the impact of artificial intelligence (AI) on the future of English language teaching and learning. Journal on Education, 6(1), 1549-1557.

Navaie, F. R., Panwar, K., & Asino, T. I. (2024). The Impact of AI-based Technology such as Chat-GPT on Second Language (L2) Learners’ Academic Writing. Proceedings of the Annual AECT Convention, 2023. The Journal of Applied Instructional Design, https://dx.doi.org/10.59668/1269.15645

Ndubueze, M., Iyoke, J. O., Okoh, S. C., & Akubuilo, B. N. (2015). Trends in Educational Evaluations in Nigeria: Issues and challenges. Journal of Education and Practice, 6(21), 71-75.

Okafor, B., & Williams, T. (2023). Enhancing English Grammar Learning Through AI: A Case Study of ChatGPT in Secondary Education. International Journal of Digital Language Learning, 5(1), 45-63.