ENGLISH REVIEW: Journal of English Education p-ISSN 2301-7554, e-ISSN 2541-3643 Volume 13, Issue 1, February 2025 https://journal.uniku.ac.id/index.php/ERJEE 67 BRIDGING THE GAP: TEACHERS’ KNOWLEDGE AND APPLICATION OF AI-DRIVEN LEARNING PLATFORMS IN STEM- BASED ENGLISH LANGUAGE EDUCATION Wahyunengsih English Language Education Department, Faculty of Tarbiyah and Teachers Training, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Indonesia Email: Wahyu.nengsih@uinjkt.ac.id APA Citation: Wahyunengsih. (2025). Bridging the gap: teachers’ knowledge and application of AI-driven learning platforms in STEM-based english language education. English Review: Journal of English Education, 13(1), 67-80. https://doi.org/10.25134/erjee.v13i1.11155 Received: 29-10-2024 Accepted: 23-12-2024 Published: 28-02-2025 INTRODUCTION AI in learning helps teachers prepare learning materials, media, and evaluation mediums. Previous studies have discussed the effectiveness of using AI in language learning. First, Ushaa et al., (2024) discovered that AI makes learning experiences more flexible and individualized, meeting each individual learner's demands and learning styles. It discusses applications of natural language processing, chatbots, speech recognition, and intelligent tutoring systems that improve language learning. Second, the AI- powered application Plang improves fluency in English speaking. The Plang builds learner autonomy through tailored feedback and sets new objectives for active involvement among Korean EFL students (Eunhyun & Juyoun, 2024). Moreover, Zang (2024) found that AI- mediated language training significantly improves learners' motivation and achievement through more engaging and individualized learning experiences. Third, well-known programs like Babbel and Duolingo use preset algorithms rather than machine learning or deep learning, where Artificial intelligence's computational capacity is recommended (Prathamesh, 2024). AI offers interactive simulations, adaptive learning, and individualized education, significantly improving language acquisition. Notwithstanding technological limitations and the requirement for pedagogical alignment when integrating AI into language instruction, teachers report increased student autonomy, engagement, and proficiency (Janet, 2024). Another study by Xiuwen et al. (2024) also examines how AI affects Chinese English language learners' language acquisition, emphasizing the mediating functions of resilience, grit, and flow in the relationship between meeting fundamental psychological needs and the desire to use AI technologies for language instruction. AI-powered language learning applications improve competence testing, instant feedback, and individualized instruction. However, they create questions regarding the integrity of academic writing, calling for both linguistic proficiency and ethical writing habits (Jian, 2024). Although there are worries about privacy, bias, and excessive dependence on technology, artificial intelligence (AI) plays a vital role in language acquisition by providing feedback, individualized learning experiences, and solutions for problems like Abstract: This study aims to determine teachers' knowledge level about AI-driven learning platforms in STEM education, to determine the extent to which language teachers apply AI-driven learning platforms in STEM education, and to see whether the level of knowledge, attitudes and readiness of teachers is balanced with its application. This study uses a quantitative and qualitative design or mixed method and applies a correlation approach. The participants of this study involved 33 English teachers in Indonesia. The results of this study conclude that the level of knowledge of language teachers on AI-driven platforms in STEM learning is still classified as intermediate. The application of AI-driven platforms in STEM learning for English teachers is also in the intermediate category. Language teachers have not yet entered the high level of application of AI-driven platforms in STEM learning, although a small number of teachers can reach this level. The correlation between teacher knowledge of AI-driven platforms in STEM learning is directly proportional to their application in learning practices. However, the data shows no correlation between teacher attitudes and readiness towards AI-driven platforms in STEM learning and the application of AI- driven platforms in STEM learning. Keywords: AI-driven platform, Teachers’ knowledge and application of AI, STEM education Wahyunengsih Bridging the gap: teachers’ knowledge and application of AI-driven learning platforms in STEM-based english language education 68 vocabulary and pronunciation (Melta, 2024). AI also improves the learning of vocabulary, grammar, and pronunciation in English language instruction. Although it provides individualized learning routes for a range of learners, it has obstacles related to infrastructure, ethics, training, and inclusivity (Iryana, 2024). Here, a research gap appears. A study to provide a more detailed picture of the use of AI in language learning is needed. Previous studies have proven the effectiveness and the obstacles to using AI without looking at the suitability of knowledge and application of AI to teachers. However, no research has described teachers' readiness to understand AI in accordance with their learning. In addition, it is necessary to study whether the application of AI in their learning is in line with their knowledge. The important rationales for discussing this topic are elaborated as follows. First, implementing AI-driven platform integration in STEM Education is a professional development skill that language teachers must have. The study of the linearization between knowledge and application in learning is critical to evaluate the process and learning products that use AI-driven platforms in STEM Education. In addition, this is also useful for improving teaching methodology and improving student learning outcomes. In the current AI era, integration improves teachers' digital competence and overcomes gaps in access to quality education. Second, in some conditions, today's students, primarily native technology, must be balanced with qualified technology literacy from teachers. AI literacy in teachers will contribute to effective integration and enable teachers and students to utilize AI for data visualization and processing (Son, 2023; Ning et al., 2024). Third, the analysis of literacy and teacher application of AI-driven platforms in STEM Education also increases students' role. Students will get a more optimal role of involvement, which will directly impact learning outcomes. AI-driven platforms offer a more personalized learning experience, increase student motivation and engagement, and are tailored to the diverse needs of each individual (Adeniyi et al., 2024). In addition, the results of this study are expected to improve the ability and creative thinking skills of both teachers and students in solving problems (Anand, 2024). Fourth, this study will look at how AI plays a role as a solution to overcome educational disparities. Teachers and students in marginalized populations and places can equally receive feedback from the professional development process and real-time feedback. In addition, English learning must be supported by a collaborative environment and media so that it can promote inclusivity (Adeniyi et al., 2024). Based on the background above, the research problems formulate as follows: (1) What is the level of teacher knowledge about AI-driven learning platforms in STEM Education? (2) To what extent do language teachers apply AI-driven learning platforms in STEM Education? (3) How does the level of teacher knowledge about AI- driven platforms correspond to their application in STEM Education? METHOD This study uses a quantitative research design with a descriptive and correlational approach. So, a mixed-methods methodology is utilized in this study. The descriptive approach aims to describe in detail the phenomena or conditions related to the level of teacher knowledge regarding the AI- driven learning platform and its application in STEM education. At the same time, the correlational approach aims to answer the question of the relationship or correlation between the level of English teacher knowledge and the quality of the application of the AI-driven learning platform in STEM education. It is in line with Creswell's (2014), which states that mixed- method research is suitable for achieving the goal of providing a more complete and holistic picture of the research problem, which shows the relationship between two variables of knowledge and the application of the AI-driven learning platform in STEM Education, while the descriptive approach is used to provide an overview of the reasons for the relationship. Participants in this study were 33 English teachers. Although the sample is relatively small, participants were selected purposively to ensure adequate representation of the population studied. They were chosen purposively by looking at their S1, S2, or S3 educational background. However, no teachers with a doctoral background could be found. In addition, the teacher's teaching experience is another indicator that shows the distribution of teacher professionalism levels based on work experience. The work experience group is divided into: < 2 years, 2-5 years, 5-8 years, 10-15 years, 15-20 years, and > 20 years. The instrument in this study is a questionnaire that refers to Schuster's theory. The questionnaire was created by involving nine indicators of knowledge and application of AI in language ENGLISH REVIEW: Journal of English Education p-ISSN 2301-7554, e-ISSN 2541-3643 Volume 13, Issue 1, February 2025 https://journal.uniku.ac.id/index.php/ERJEE 69 education. These indicators include using technology to personalize learning, improving critical thinking skills, support global collaboration, expanding access and inclusivity, improve adaptive learning and language learning experiences, more individual, contextual and relevant learning to needs, and utilization in data processing. The data ware analyzed quantitatively and qualitatively after collecting data through an online questionnaire using a Google form. The quantitative analysis process was carried out to calculate the frequency, percentage, average, and distribution of answers to see teachers' general pattern of knowledge and application of AI. Then, the Pearson correlation test was used to measure the strength and direction of the relationship between knowledge and application of the AI- driven learning platform in STEM learning. The second qualitative analysis process was carried out. Qualitative analysis was carried out to identify recurring themes or patterns related to teachers' knowledge and application of AI. Coding of relevant data questionnaires and analysis of appropriate themes to answer the problem formulation are also needed at this stage. RESULTS AND DISCUSSION The result of the questionnaire shows the demographic data from participants in this study can be seen in the table below. Table 1. Teacher education level Education Level Percentage Strata 1 75% Strata 2 25% Strata 3 0% Total 100% There were 33 teachers involved in this research. It can be seen in the table that 75% of them have a bachelor's degree, which means around 25 people from the total participants. There are 25% with postgraduate degrees, or around 8 teachers have a 2nd degree background. This finding is also in line with Yeh (2024) which shows that junior teachers tend to be more open to the use of technology, especially AI-based platforms for STEM education. It indicates that experience based training is crucial to maximalize teachers’ potential from various level of experiences. Ejjami (2024) also clearly stated in his study that extensive professional development programs for educators, equal access to AI technologies, and the establishment of solid ethical criteria were mostly joined by younger teachers. In other words, the results of this study can have implications for improving teachers' technological literacy by involving policy makers and the government. Regular technology workshops and project-based training provide hands-on experience in using AI technology between generations of teachers need to be implemented consistently and measurably. Since work experience is one of important aspect of professionalism, the survey also required the participant to mention this information. The following table also provides the distribution of the teachers’ work experience. Table 2. Work experience Work Experience Percentage < 2 years 61% 2-5 years 21% 5-8 years 3% 8-10 years 6% 10-15 years 3% 15-20 years 3% > 20 years 3% Total 100% Based on the data above, 82% of the participants, or 26 out of 33 teachers, have 2 to 5 years of teaching experience. It shows that the level of participation in this research on AI-driven platforms in STEM learning tends to be more young teachers. However, the distribution also indicates that all levels of teaching experience are covered in the questionnaire. This finding aligns with research conducted by Yeh (2024), which shows that young teachers tend to be more open and comfortable using STEM learning and AI- driven platforms. Another study also found that pre-service English language teachers (PELTs) were more enthusiastic about using AI because they saw the opportunity for this tool to be able to personalize learning and significantly improve students' communication skills (Yetkin & Özer- Altınkaya, 2024). In addition, teachers with more extended teaching experience tend to focus on ethical implications and student potential rather than relying on technology (Karta, 2023). The development of AI-based modules, as well as training and mentorship between young and senior teachers, will positively improve the skills and mastery of STEM teachers in learning. Challenges such as lack of confidence and anxiety in senior teachers less familiar with technology will not be a significant challenge. It was conveyed by Ayanwale et al. (2024), who figured out that anxiety and preferred methods to increase trust and PB influence trust in AI-based edtech do not strongly influence the ability to use AI. Wahyunengsih Bridging the gap: teachers’ knowledge and application of AI-driven learning platforms in STEM-based english language education 70 In this survey, the information is divided into several parts. First, the survey material aims to see the level of knowledge of AI-driven platforms by language teachers in education. The second part looks at teachers' attitudes and readiness to use AI. In the third part, the survey aims to see the frequency of using AI in STEM learning. The following table 3 shows the results of teachers' knowledge about AI in education. The application of AI sometimes also imposes a rigid framework that limits creative thinking and innovation, leading to emotional detachment due to the repetitive and impersonal nature of AI interactions (Lin and Chen, 2024). Therefore, it is necessary to study how much knowledge teachers have about AI and their experience through self- taught learning. The codes stand for CDA = completely disagree, DA = disagree, DB = doubt, A = agree, and CA = completely agree. Table 3. Teachers’ knowledge of AI-driven platforms in STEM Education Statements Amount of Respond CDA DA DB A CA Total Equivalent score 0 1 2 3 4 1. I understand the basic concepts of artificial intelligence (AI) and its application in the field of education. 0 1 4 20 7 33 2. I know how AI can be used to personalize student learning. 0 1 4 23 5 33 3. I understand the role of AI in providing formative feedback to students in real-time. 0 1 4 23 4 33 4. I realized that AI could help analyze student learning patterns to improve learning outcomes. 0 1 5 6 21 33 5. I am aware of AI-driven platforms used in education (e.g. Khan Academy, Google Classroom AI tools). 0 1 1 22 10 33 6. I understand the risk of algorithmic bias in AI technology used in education. 0 0 10 16 7 33 7. I understand the importance of maintaining student data privacy when using AI-driven tools. 0 0 2 13 18 33 8. I know how AI can help teachers in compiling learning materials. 0 0 2 18 13 33 9. I understand that AI cannot completely replace the role of teachers. 0 0 1 7 25 33 10. I understand how AI can be used to support inclusive education (for example, helping students with special needs). 0 1 9 14 9 33 11. I know how AI can be integrated into the language learning I teach. I understand how apps like Grammarly can help students write texts in English/Other apps for the languages I teach. 0 1 9 14 9 33 12. I understand how apps like Grammarly can help students write texts in English/Other apps for the languages I teach. 0 0 1 11 21 33 13. I know how ChatGPT can be used to improve students' speaking skills in STEM contexts. 0 0 8 15 10 33 14. I know the benefits of Photomath in supporting learning to understand terms in English. 0 1 12 14 6 33 15. I once used Labster to explain a science experiment in English 0 6 15 7 3 33 Total answer 0 14 87 223 168 495 Percentage of the knowledge level 0% 3% 18% 45% 34% Note: 4 = High level of knowledge of AI-driven platforms in STEM education 3 = Intermediate level of knowledge of AI-driven platforms in STEM education 2 = Basic level of knowledge of AI-driven platforms in STEM education 0-1 = Low level of knowledge of AI-driven platforms in STEM education Based on the data above, 34% of the teachers have a high knowledge of AI-driven platforms in STEM education. 45% of the teachers can be categorized into intermediate level. 18% of them are basic, and only 3% of the total samples can be classified into low-level knowledge of AI-driven platforms in STEM education. In other words, most language teachers still have an intermediate understanding of AI-driven platforms in STEM education. This finding even shows an ENGLISH REVIEW: Journal of English Education p-ISSN 2301-7554, e-ISSN 2541-3643 Volume 13, Issue 1, February 2025 https://journal.uniku.ac.id/index.php/ERJEE 71 improvement compared to research conducted by Chang & Tang (2024), which found that English teachers still can understand at a basic level regarding AI-driven tools. English teachers still lack the latest technological pedagogical knowledge (TPK), essential for AI integration in education. On the other hand, preservice teachers show better readiness in using AI but are also not supported by limited capabilities in this field. Several reasons can be explained regarding this finding. First, teachers still have inexperienced knowledge of AI, which is still relatively new and rapidly developing (Sabaruddin et al., 2024). Teachers must be provided with adequate training related to this field, which is carried out periodically (Minnillo et al., 2024). Third, various challenges in integrating AI-driven platforms in STEM learning include limited access to technological resources, varying student ability levels, and administrative constraints (Sabaruddin et al., 2024). Another study conducted by Buthaynah (2024) also found that the ability of language teachers in using ChatGpt and Duolingo clearly assisted by the AI, yet most of the language teachers need to improve their language ability itself. Furthermore, it seems a global issue among all language teachers due to several factors. First, English teachers still lack experience integrating AI technology into language teaching. Mastery and knowledge of AI-driven platforms require ongoing training to improve teachers' professional competence and increase their teaching efficacy (Zada et al., 2024). Second, technical capability is critical in AI, especially in learning evaluation. This study suggests that English teachers should not integrate AI into their teaching practices (Hao, 2024). Third, the ability of English teachers at AI is still classified as intermediate due to the imbalance in the development of six dimensions of competence: linguistic, pragmatic, socio- linguistic, discursive, strategic and cultural competence (Jan et al., 2024). Fourth, this intermediate level reflects teacher familiarization with AI technology, which is the impact of critical thinking and originality on teacher professional development (Valentyna et al., 2024). Apart from that, obstacles come from the lack of institutional support and advanced strategies to improve the understanding and application of AI in language learning (Pooja & Rames, 2024). Therefore, further pressure and demands in advanced or tertiary education curricula are needed to increase awareness of AI's ethical and practical implications in ELT (Nicky, 2023; Carol et al., 2024). According to Mutambik (2024), the transformative potential of AI as an alternative path to continuing education and streamlining the operation of learning management systems (LMS) requires fast information update skills with relatively short adaptation times. Teachers with a large workload give them less free space to improve their knowledge in this rapidly changing field. Table 4. Teachers’ Knowledge of AI-driven platform in English language evaluation Statements Amount of Respond CDA DA DB A CA Total Equivalent score 0 1 2 3 4 1. I know how AI can help in the evaluation and assessment of English language learning. 0 0 5 27 1 33 2. I belief that AI can increase evaluation efficiency in language learning. 0 0 5 28 0 33 3. Teachers can give direct feedback personally to the students in each assignment. 0 0 11 20 2 33 4. AI use in formative assessment can replace the role of teachers in scoring 9 8 9 12 3 33 5. AI can evaluate tests automatically and give more accurate score 0 0 11 21 1 33 6. AI can give objective assessment in English language test without bias 0 0 5 23 5 33 7. AI can help to score students speaking skill 0 0 3 17 13 33 8. I belief that the use of AI in English assessment and evaluation can be done without breaking the ethical rules. 0 3 5 24` 1 33 Wahyunengsih Bridging the gap: teachers’ knowledge and application of AI-driven learning platforms in STEM-based english language education 72 9. I belief that the AI algorithm is valid and not bias 0 7 7 16 3 33 10. AI can help to detect students’ mistakes and help them to revise it 0 2 8 11 12 33 11. I still face many challenges in using AI for evaluating student’s English competency but can find solution 0 0 7 15 11 33 12. I belief that using AI in language learning evaluation will help my professional development skill 0 1 3 18 11 33 13. I need more training in using AI effectively in Language assessment and evaluation 0 0 4 21 8 33 14. AI has limitation in evaluating complex aspects of English language especially in speaking and writing skill 0 0 4 25 4 33 15. The use of AI in English language evaluation will cause challenges in cultural nuance 0 0 6 23 4 33 16. AI cannot always replace the teacher’s role in giving holistic and emotional evaluation 0 0 3 27 3 33 17. I know the function of Grammarly and Turnitin in English evaluation 0 0 6 20 7 33 18. I understand the function of Socratic by Google which can help analyze student questions 0 0 8 17 8 33 19. I am familiar with the use of ProWritingAid to thoroughly analyze text quality and provide feedback 4 1 15 10 3 33 20. I understand the use of Voxy to assess students' speaking and listening abilities 3 3 7 12 7 33 21. I am proficient in using Speechace as a medium for analyzing student pronunciation and providing direct feedback 1 1 9 17 5 33 22. I can use Quillbot to help provide feedback on writing 0 0 3 25 5 33 23. I often use Lingvist to assess students' vocabulary abilities and provide exercises that are relevant to students' abilities 6 2 10 12 3 33 24. I know the mechanism for using Cambridge English Write and Improve to provide automatic assessment of students' writing results 3 2 9 18 1 33 25. I can use WriteLab for in-depth assessment of students' writing abilities 1 1 11 15 5 33 26. I know the function of the Learning Management System (LMS) which uses AI to provide analysis of student learning outcomes 0 3 5 21 4 33 27. I use Magoosh as an automatic assessment tool on practice questions 0 5 12 11 5 33 28. I am comfortable and feel the benefits of using AI- driven Chatbots such as ChatGPT in assessing student assignments and tests 0 0 9 21 3 33 29. I know how Gradescope can help to evaluate students’ assignment in STEM education 0 3 12 15 3 33 Total answer 27 42 212 518 141 957 Percentage of the knowledge level 2.8% 4.4% 22.6% 55.2% 15% The data shows that teachers’ knowledge of the use of AI-driven platforms for language evaluation in STEM education is various. 7.2% of teachers can categorize into a low level of knowledge. The percentage of the basic level is relatively high, at 22.6%. The similarity in teachers’ understanding of AI-driven platforms in STEM education and language evaluation is at the intermediate level. This level still places the highest among the other levels. However, the high level is significantly decreased here. It reaches 15% over the total of the samples. The use of technology in learning evaluation will undoubtedly provide convenience in terms of ENGLISH REVIEW: Journal of English Education p-ISSN 2301-7554, e-ISSN 2541-3643 Volume 13, Issue 1, February 2025 https://journal.uniku.ac.id/index.php/ERJEE 73 the effectiveness of checking student assignments. AI also can empower educators, personalize learning, and address achievement gaps, offering a nuanced perspective on the complex tapestry in education (Dutta, 2024; Aggarwal, 2023; Ray & Sikdar, 2024; Wu et al., 2024; Mavropoulou et al., 2024). However, an interesting fact from the findings of this research shows that teachers have lower ability and understanding regarding the use of AI-driven platforms in STEM learning compared to their knowledge of AI in learning. Several previous studies explain this. Firstly, teachers often face challenges in gaining adequate skills in this matter. The gap in technological knowledge is one of the main reasons. Teachers worry about the integrity and originality of AI assessment results (Lukianenko & Kornieva, 2024). Apart from that, there is also an explanation of the generative impact of AI on students' critical thinking and creativity, which adds to the complexity of using AI as an evaluation tool (Yang, 2024). Besides technological gaps, which are the leading cause of English teachers' low understanding of using AI-driven platforms for evaluating STEM learning, pedagogical and infrastructure barriers also play a significant role. Many teachers do not know about technology that can be used effectively and evaluate language (Sabaruddin et al., 2024). Integrating AI in language learning evaluation poses significant challenges for teachers due to technological, pedagogical, and infrastructural barriers. These difficulties hinder the effective implementation and utilization of AI tools in educational settings. Technology training and socialization programs also require practice and a long understanding period. It is not uncommon for human resources who can be tutors in using AI for learning evaluation in various places to be very limited. Online training cannot provide comprehensive and in-depth AI media practice experience intensively, while evaluation requires high validity and accountability. It makes most teachers give up their intention to use AI as an evaluation tool for STEM education. In addition, evaluation-based AI is often paid, and schools do not provide support for adopting this tool (Syarifudin, 2024; Rane & Rane, 2023; Abu-Ghuwaleh, 2023; Strielkowski et al., 2024; George, 2023). Furthermore, ethical and pedagogical concerns related to the ethical implications of AI, which is sometimes problematic for data privacy and has the potential for bias in algorithms, also reduce teachers' interest in using it. Moreover, this will be a rejection for teachers who are used to and feel comfortable with conventional evaluation techniques (Aljabr & Al- Ahdal, 2024). Warschauer in Assefa (2024) also stated that the fact that technology of AI always has flaws which provide potential benefits in personalizing learning but at the same time potentially loss human element in practice. AI is often an alternative to innovative and inclusive education. However, it also usually refers to problematic conditions and gaps, especially in large and diverse countries (Murtaza et al., 2024; Ouyang & Zhang, 2024; Yahania, 2022; Castro et al., 2024; Ayeni et al., 2024). When teachers use AI and realize the risk, they tend to stop and avoid problems. It may lead to finding another better and suitable tool, but not often ended using the AI and getting back to traditional ways. As mentioned in some previous research which highlights barriers, including teacher trainings which basically focus on developing the teachers’ knowledge in the use of AI in education (Soelistiono & Wahidin, 2023; Yilmaz, 2024; Huang, 2024; Jamro & Jamro, 2023; Kong et al., 2024). Next, the data from the questionnaire also reveals the teachers’ application of AI-driven platforms in STEM education. There are seventeen statements addressed to the teachers. It also uses the Likert scale to classify the teachers’ answers. All of the statements cover the information related to teachers’ attitudes to receiving new technology, teachers’ motivation to learn new technology in AI-driven platforms, teachers’ act on the use of AI in STEM education, teachers’ active participation in evaluating the advantages and disadvantages of the AI, teachers’ collaboration with the students, colleagues, and parents, teachers’ management in using AI during teaching and learning processes as well as evaluation, teachers’ plan and application in following and keeping the ethical guidelines and safely manage the students’ data. All the teachers’ responses in the questionnaire are also recorded with the scoring. Completely disagree = 0, disagree =1, doubt = 2, agree = 3, completely agree = 4. The total percentage of 4 score will determine the high level of positive attitude and application of AI in STEM education, 3 for intermediate, 2 for basic and 0-1 means low attitude and application. The results of the questionnaire are elaborated on Table 5: Table 5. Attitude and readiness in using AI-driven Platforms in STEM education Amount of Respond Wahyunengsih Bridging the gap: teachers’ knowledge and application of AI-driven learning platforms in STEM-based english language education 74 Statements CDA DA DB A CA Total Equivalent score 0 1 2 3 4 1. I feel confident using AI applications in language teaching. 0 0 2 22 9 33 2. I believe that AI can improve the effectiveness of Language teaching in STEM Education. 0 0 5 19 9 33 3. I am ready to take part in training to further understand the use of AI in learning. 0 0 3 12 18 33 4. I am very enthusiastic about following the development of AI in learning and am ready to apply it in my learning process. 0 0 1 15 17 33 5. I am passionate about exploring new AI and discovering its benefits for learning. 0 0 2 18 14 33 6. I find out what type of AI best suits students' needs. 0 0 1 21 11 33 7. I use AI to help students improve their language skills, both receptive and productive skills in STEM material. 0 0 1 17 15 33 8. I am positive that the use of AI can help teachers provide objective, data-based assessments of STEM learning. 0 0 3 12 18 33 9. I utilize AI to provide personalized feedback to each student quickly and accurately 0 0 3 19 11 33 10. I am able to seamlessly manage and utilize AI to support language learning in STEM contexts. 0 0 2 12 19 33 11. I don't feel new technology burdens me and focus on its benefits to support student learning. 0 0 2 14 17 33 12. I am open to sharing my knowledge and experiences with colleagues, students and parents in language learning in STEM contexts. 0 0 3 12 18 33 13. I motivate students to be enthusiastic about using AI that allows students to collaborate. 0 0 2 13 19 33 14. I am careful and ensure the use of AI in Language learning in STEM contexts is safe and follows good ethical guidelines. 0 0 1 13 19 33 15. I know how to keep student data secure when using AI. 0 0 8 15 10 33 16. I actively assess the effectiveness of AI-driven platforms which have many benefits for student learning progress. 0 0 1 12 18 33 17. I use the insights that AI provides to adapt my teaching methods and strategies 0 0 0 11 22 33 Total answer 0 0 40 257 264 561 Percentage of the application level 0% 0% 7% 46% 53% The results show that 53% of teachers are categorized as high-level in attitude and readiness of using AI to STEM education. This figure is quite dominant, followed by 46% falling into the intermediate category. Only 7% are in the basic category. The questionnaire results show that, on average, teachers have good attitudes and readiness to use AI-driven platforms in STEM learning. It means that language teachers have high motivation and are eager to study and apply AI in STEM learning. In other words, the finding shows teachers have excellent attitudes and readiness towards using AI-driven platforms in the STEM learning context. This finding is not that surprising. Previous studies also prove that language teachers' attitudes and readiness are very good for using AI-driven platforms in STEM education. Many logical reasons can explain and strengthen this finding. The first is the potential of AI as superior scaffolding for complex subjects. Teachers realize AI will make things easier (Kim & Kim, 2022). Second, the presence of easy and cheap platforms for new users is a big motivation for teachers to be confident using them (Yao & Huang, 2024). The teacher's focus on teaching and research motivates good attitudes and readiness. AI will help teachers organize academic and research tasks more effectively and efficiently (Varghese & Selvaraj, 2024). Language teachers are also familiar with Technological Pedagogical Content Knowledge (TPACK), a supporting basis for AI integration (Sun et al., 2024; Nur et al., 2024). Next, the system supports the teachers in getting ENGLISH REVIEW: Journal of English Education p-ISSN 2301-7554, e-ISSN 2541-3643 Volume 13, Issue 1, February 2025 https://journal.uniku.ac.id/index.php/ERJEE 75 more straightforward and accurate assessments. According to the systematic study, AI-based STEM educational assessment used both conventional algorithms (like machine learning and natural language processing) and sophisticated algorithms (like deep learning and neural fuzzy systems (Ouyang et al., 2024; Joseph & Uzondu, 2024; Luzano, 2024). Moreover, when paired with pedagogical knowledge (PK), which is reflected in technical pedagogical knowledge (TPK), TK becomes significant for instructors to use AI in education effectively and with a positive attitude (Yang et al., 2024; Zhai & Krajcik, 2024; Amdan ET AL., 2024; Nagaraj et al., 2023; Olatunde-Aiyedun, 2024; Celik, 2023). Parfiz (2024) also clearly supports it by stating the application of AI-based learning platforms in STEM education provides guidelines for the moral and successful incorporation of AI technology in the classroom. Furthermore, the results of the questionnaire figure out teachers’ application of the AI-driven platforms in STEM education. Here, the scale is different than the knowledge and attitude of AI. There are five scales: always = 5, Often = 4, Sometimes = 3, Rarely = 2, never = 1. There are twenty statements which are divided into seven parts. Table 6 shows more comprehensive data. Table 6. Teachers’ application of AI-driven platforms in STEM education Statements Amount of Respond N R S O A Total Equivalent score 0 1 2 3 4 A. Use of AI for Language Practice 1. I use Grammarly/other similar AI to help students improve their grammar and spelling in their writing. 3 0 8 15 6 33 2. I use QuillBot/other similar AI to help students rephrase English sentences. 4 1 9 12 7 33 3. I use Duolingo/other similar AI to help students learn STEM vocabulary in English. 4 3 13 8 4 33 B. Use of AI for Pronunciation and Listening 4. I use ELSA Speak/similar AI to help students practice their English pronunciation or Indonesian/other foreign language pronunciation. 7 4 11 8 3 33 5. I use Google AI Text-to-Speech/other similar AI to support students' understanding of STEM terms in English or Indonesian/other foreign language pronunciation. 4 4 10 12 3 33 6. I use AI-driven applications to provide listening materials on STEM topics. 2 4 12 14 1 33 C. Using AI for Reading and Writing 7. I use ReadTheory to improve students' reading skills on STEM- related topics. 9 7 12 3 2 33 8. I use Labster to help students read and understand science simulation instructions in English. 14 5 11 3 0 33 9. I use Turnitin to evaluate students writing assignments to analyze the originality 7 3 5 10 8 33 D. Using AI to Create Interactive Content 10. I use Canva with AI features to create interactive presentations on STEM topics in English. 1 2 5 9 16 33 11. I use Kahoot! to create interactive English-based quizzes related to STEM. 2 3 7 6 15 33 12. I use other AI applications to create engaging learning materials for students. 1 2 3 14 13 33 E. Using AI for Learning Evaluation 13. I use Gradescope to help provide feedback on student assignments in English. 11 5 8 6 3 33 14. I use AI applications to assess students' mastery of STEM technical terms in English. 9 5 6 8 5 33 F. Frequency of AI Application in STEM Learning 15. I apply AI in every English class meeting 4 5 16 6 2 33 16. I use AI applications to adjust learning materials 1 6 8 11 4 33 Wahyunengsih Bridging the gap: teachers’ knowledge and application of AI-driven learning platforms in STEM-based english language education 76 to students' needs. 17. I use AI to help students prepare for STEM-based projects in English. 3 2 16 8 4 33 G. Barriers to AI Implementation 18. I find it difficult to use AI due to the lack of technological infrastructure in the school. 6 6 11 4 6 33 19. I have limited time to learn and apply AI in learning. 5 5 13 6 3 33 20. I feel that there is a lack of support from the school in using AI applications. 7 8 12 3 3 33 Total 104 80 196 166 108 660 Percentage of teachers’ application of AI 16.5% 12.2% 29.4% 25.3% 16.5% It shows that only 16.5% of the teachers highly apply AI in their STEM education. 25.3% can be categorized into the intermediate level of AI application, 29.4% are at the basic level. 28.7% of the teachers are at the lowest level of AI-driven platforms application in STEM education. However, in this section the distribution of answers is quite even in each section. None of the answer scale. There is no scale that shows the number 0%. but in this section the distribution of answers is quite even in each section. There is no scale that shows the number 0%. However, the highest level of teacher application on the AI- driven platform is at the basic level although it is not much different from the intermediate level, which is only 4.1%. Now, to figure out the correlation between teachers’ knowledge about AI-driven learning platforms balanced with their application in STEM Education, Person correlation coefficient is needed to answer it. Here, the scores used are the total percentage of each variable. More detail is shown on Table 7 below. Table 7. Correlation of knowledge and application of AI-driven platform in STEM education Correlations Knowledge Application Knowledge Pearson Correlation 1 .679 Sig. (2-tailed) .207 N 5 5 Application Pearson Correlation .679 1 Sig. (2-tailed) .207 N 5 5 The sig. value between knowledge and the application is 0.207. It is < 0.05. It means there is correlation between these two variables. The value of the person correlation shows that 0.679 which is between 0.61 to 0.80 that means has strong correlation. It can be concluded that the higher teachers’ knowledge on AI-driven platforms in STEM education, the higher the application of AI-driven platforms in STEM education. So, the third research question is clearly answered: the level of teacher knowledge about AI-driven learning platforms balanced with their application in STEM Education. Furthermore, it is also crucial to figure out the correlation between the teachers’ attitude and readiness of AI-driven platforms in STEM education with the application of the AI-driven platforms. Here is the result of the statistical computation. Table 8. Correlation of teachers’ attitude and readiness and application of AI-driven platform in STEM education Correlations Attitude and Readiness Application Attitude and Readiness Pearson Correlation 1 .170 Sig. (2-tailed) .784 N 5 5 Application Pearson Correlation .170 1 ENGLISH REVIEW: Journal of English Education p-ISSN 2301-7554, e-ISSN 2541-3643 Volume 13, Issue 1, February 2025 https://journal.uniku.ac.id/index.php/ERJEE 77 Sig. (2-tailed) .784 N 5 5 Based on the data, it shows that the sig. value of attitude and readiness and application is 0.784 which is > 0.05. It means there is no correlation between the teachers' attitude and the teachers’ application of AI-driven platforms in STEM education. Meanwhile, the value of the person correlation shows that 0.170 which is in between 0.00 to 0.02 which means no correlation. The conclusion is high level of teachers’ attitudes and readiness to AI-driven platforms does not determine the high level of the application of the AI-driven platform in STEM education. The rationales of the finding can be explained by this following discussion. The fact that there is a strong correlation between teachers’ knowledge to the application is in line with Sun et al. (2024) that stated integrating AI in STEM education closely relates to teacher knowledge and competence. It is undoubtedly greatly influenced by the level of teacher understanding of technology adoption in the learning process and evaluation. Technological Pedagogical Content Knowledge (TPACK) affects the effectiveness of teachers in using AI quickly and smoothly. It increases their self-efficacy and willingness to integrate AI into their teaching practices continuously. Teachers' professional knowledge in STEM education is positively correlated with their performance. In other words, their knowledge will affect the level of AI utilization more effectively (He, 2024). Moreover, the use of AI in STEM education is also directly proportional to teacher knowledge due to several factors. First, teachers must understand AI before integrating it into the curriculum and use it effectively to meet all students' needs and provide solutions to their problems (Nam et al., 2022). In addition, integrating AI into teaching practices requires a good understanding of AI itself so that it can engage students (Emmanuel, 2021). Knowledge will also enable personalization and increase student engagement in overcoming challenges and obtaining related resources and training as needed (Oktian et al., 2024). Ariane et al., (2024) also elaborated that the application of the AI by teachers in their teaching and learning process is one of the problems solving methods. Moreover, AI does not only personalize learning but also personalize teaching itself. Therefore, knowledge and comprehension are closely related (Malgorzata (2024). In the other side, there is no correlation between the teachers’ attitude and readiness of AI-driven platforms with the application in STEM education also in line with some theories. First, Oluwanive & Kok (2024) found that attitudes and readiness towards AI and its implications for the application of AI in STEM education are complex. It is because AI was not created by a factory specifically for educational practice purposes. It also causes positive attitudes and readiness to have no direct influence on the application of AI in learning. In addition, TPACK knowledge is also the background that causes AI applications not to run in balance with their application. It is also because using AI requires an independent learning process and often lacks tutor resources or organized training. Teachers must be ready to solve problems and find solutions to them independently. In addition, Musa & Sanusi (2023) also mentioned the lack of a support system for teachers in the implementation makes the implementation process imperfect and often does not match the teacher's initial plan in STEM education. CONCLUSION Based on the findings, this study concludes that language teachers' knowledge of AI-driven platforms in STEM learning remains intermediate. Similarly, their application of AI-driven platforms in English language instruction within STEM contexts also falls within this category. Most teachers have not yet wholly incorporated AI- driven tools into their teaching approaches. However, a small number have advanced. Furthermore, the study demonstrates a direct correlation between teachers' familiarity with AI- driven platforms and their classroom use. However, no significant correlation was found between teachers' attitudes and readiness towards AI-driven platforms and their actual implementation in STEM learning. Future studies can examine the obstacles preventing educators from integrating higher-level AI. A more profound knowledge of how instructors could be better equipped for AI-driven instruction may be obtained by looking at factors including training accessibility, institutional support, and the effectiveness of professional development programs centred on AI. The effects of extended exposure to AI technologies on instructors' confidence and pedagogical approaches may potentially be investigated through longitudinal research. 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