CC BY
19УДК 37
Taniyeva A.I.
master of pedagogical sciences, assistant of lecturer Kokshetau University named after Sh. Ualikhanov (Kokshetau, Kazakhstan)
THE INFLUENCE OF ARTIFICIAL INTELLIGENCE ON LEARNING ENGLISH
Аннотация: this study examines the influence of artificial intelligence (AI) on learning English, highlighting its integration in educational practices and its impact on learners. AI applications have become integral to various sectors, and their potential in education has led to significant investments aimed at transforming the learning experience. Despite these advancements, challenges remain, particularly the lack of pedagogical understanding among AI developers and the underrepresentation of educators' perspectives. This research aims to bridge these gaps by exploring the benefits and challenges of AI in English language education. The findings suggest that AI can enhance learning by providing personalized and adaptive experiences, improving students' vocabulary and speaking confidence. However, limitations such as the absence of human interaction and the needfor extensive data for training AI models are also noted. A hybrid approach combining AI and traditional teaching methods is recommended to maximize the benefits of AI in education.
Ключевые слова: Artificial Intelligence, English Language Learning, Education Technology, Personalized Learning, Adaptive Learning, AI in Education, Pedagogical Practices, Educators' Perspectives, Hybrid Learning Approach, Language Proficiency.
Artificial intelligence (AI), often unnoticed in our daily lives, permeates it through numerous mechanisms. Whether it is searching on Google, reading emails, scheduling a doctor's appointment, requesting a travel route, or receiving recommendations for films and music, we are in constant interaction with applications that employ artificial intelligence. The necessity for the further advancement of AI systems became especially evident during the COVID-19 pandemic, when AI demonstrated its efficacy in sectors such as healthcare, education, communication,
transportation, and agriculture. Applications powered by AI have evolved from merely useful innovations to essential components of contemporary life.
The rapid development of AI technologies exerts a profound impact on both pedagogical practices and the educational process as a whole. According to numerous projections, the education sector is poised for a significant transformation through the integration of AI [1, p. 115-125].
This potential has driven significant investments aimed at integrating AI into the educational landscape [2, p. 4,488]. However, a notable challenge hindering the successful implementation of AI in education is the predominantly commercial focus of current AI applications in this field. AI developers often lack a comprehensive understanding of learning sciences, and their pedagogical knowledge may be insufficient for effective AI integration [3, p. 1595-1612]. Additionally, AI developers frequently overlook the needs and expectations of end-users in education, particularly educators [4, p. 61-75] . Teachers represent a crucial group of stakeholders in AI-assisted learning [5, p. 2022]. Their perspectives, experiences, and expectations are vital for the successful integration of AI in educational institutions [6, p. 1-17].
To make AI pedagogically beneficial, a deeper understanding of the advantages it offers to teachers and the challenges they face when integrating AI into their teaching practices is necessary. Unfortunately, the perspectives of educators on AI-enhanced education are largely ignored. Moreover, teachers' competencies in the pedagogical use of AI and their role in developing pedagogical AI tools are underrepresented in scientific research. This study aims to address these gaps by examining the potential and challenges of AI in pedagogical practice identified in existing studies. As the field of AI-assisted learning is still evolving, this research seeks to contribute to the creation of comprehensive AI-supported educational systems that involve teachers in their design and implementation.
The extent to which artificial intelligence is integrated into the planning and execution of educational processes, from primary schools to higher education institutions, remains a critical issue within the education system. A key question is
whether the active utilization of artificial intelligence in educational contexts will yield more benefits than drawbacks.
Before conducting a comparative analysis of the advantages and disadvantages of artificial intelligence, it is essential to define the term. The literature presents various interpretations of this term. For instance, Artificial intelligence (AI) is increasingly interpreted as the capability of automated systems to perform certain functions traditionally associated with human intelligence. Even the development of the simplest AI models requires knowledge from multiple scientific disciplines. Consequently, in a broader sense, AI encompasses a set of models, methods, and technologies designed to address poorly formalized (intellectual) problems [7, p. 25-30].
In recent decades, various new educational technologies have emerged, with artificial intelligence (AI) being the latest [8, p. 43-52]. Baker and Smith clarified that AI is not a single technology but rather «computers performing cognitive tasks typically associated with the human mind, particularly learning and problem-solving» [9, p. 3].
Technological tools used in conjunction with AI systems in education can appear quite familiar, such as interactive whiteboards, informational kiosks, and other informational systems. However, the modernization of educational institutions is also introducing interactive systems, including robotic assistants and other innovative forms. The integration of automated systems employing artificial intelligence allows for the enhancement of existing functions of conventional tools.
AI applications in education are diverse but are all aimed at ensuring a high-quality learning process by creating a comfortable environment for both educators and students.
Intelligent assessment is a promising area in the evaluation of student work. By mimicking the behavior of educators, automated systems can objectively assess the provided data. By analyzing students' knowledge based on their responses and the material covered, the system can offer necessary feedback.
Intelligent virtual assistants leverage accumulated data and information available on the internet to answer questions from both students and educators. Real-
time data on student performance enables the adjustment of personalized learning plans, and a provided campus map can assist in navigating the educational institution more effectively.
Personalized and adaptive learning involves automatic adjustment of the learning schedule based on current performance levels, as well as notifying educators of potential difficulties in mastering the educational program. This allows the system to search for necessary materials and provide additional support to participants in the educational process.
AI as an educator's assistant can help find material for practical and lecture sessions based on the curriculum provided by the educator. Using communication tools, the system can present material, play video and audio clips, and enhance accessibility and comprehension. With internet access, the system can analyze questions and answers during class and provide instant feedback.
I systems in education can understand human speech and conduct dialogues with students in any language, enhancing the educational experience, broadening vocabulary, and correcting errors interactively. Intelligent knowledge assessment systems help educators by grading certain assignments, allowing them to focus on enriching their teaching materials. Companies like Educational Testing Service and Pearson use natural language processing to evaluate essays [10, c. 2824-2838]
The use of AI language learning technologies is also fraught with a number of difficulties and restrictions:
1. Lack of human interaction: According to Khanzode and Sarode, the primary drawback of AI language learning technologies is the absence of human connection. While some resources allow users to practice live conversations with tutors or native speakers, the majority of learning activities are self-directed and do not require face-to-face communication. [11, p. 116] For students who want a more individualized and participatory learning environment, this may be an issue.
2. Dependence on large amounts of data for training: AI language learning technologies may require assistance in order to accurately mimic the cultural and contextual nuances of language, including idioms, colloquialisms, and regional
accents. This might result in communication problems or misunderstandings, especially when using more complicated or technical jargon.
3. Reliance on vast volumes of data for training: AI methods for learning languages are dependent on vast volumes of data for training, which may be a problem for languages or dialects that are not widely spoken. This may result in a dearth of tools or skewed educational materials for these languages.
4. Limited capacity to comprehend or produce creative or original language: AI language learning systems can require assistance to comprehend or produce literary works like poetry or fiction. Additionally, they could require assistance with assignments requiring a high degrof language skill, including sophisticated vocabulary or grammar.
5. Limited capacity to identify mistakes Limited error recognition: Artificial intelligence (AI) language learning systems might not be able to identify or fix mistakes as precisely as a human tutor or teacher. This may cause students to form undesirable habits or do the same mistakes repeatedly [12, p. 106-552].
These tools offer advantages such as time savings, accelerated learning, personalized education, and exposure to diverse cultures. However, they also present challenges, including the lack of human interaction, difficulty capturing cultural and contextual nuances in language, and the substantial data needed to train AI effectively.
Current technology has not yet achieved consciousness, and its application in innovative and creative ways, particularly in education, is still underutilized. The integration of AI in the classroom is in its early stages, with active research and exploration by educators and scholars.
Flipped Learning, a pedagogical approach gaining attention, positively impacts academic performance, student attitudes, collaborative learning, and self-directed learning. In English language instruction, it enhances metacognitive abilities, involving understanding and regulating cognitive processes. Flipped Learning also promotes spontaneous student engagement, reinforcing self-efficacy—the belief in one's ability to complete tasks successfully.
However, applying Flipped Learning in English language classrooms presents challenges. Integrating Movie-based learning with Flipped Learning could provide a more effective pedagogical strategy in English instruction.
The survey, conducted among first-year students, revealed insightful perspectives on the use of AI for language learning. A significant number of respondents reported that interacting with AI reduced their anxiety related to speaking, which they often experience in classroom settings with teachers. The convenience of practicing at home and at times that fit their schedules was noted as a major advantage. However, approximately half of the students emphasized the importance of receiving feedback and grammatical corrections, which they found lacking in AI interactions. Moreover, they expressed concerns about the potential monotony and decreased motivation associated with the continuous use of AI for language practice, indicating that it is not a permanent substitute for traditional learning methods.
Expanding on these findings, it becomes evident that while AI can serve as a valuable tool in alleviating initial speaking apprehensions and providing flexible learning opportunities, it cannot fully replace the interactive and corrective role of human educators. The integration of AI in language learning must, therefore, be balanced with methods that ensure comprehensive feedback and maintain student engagement. This suggests a hybrid approach, combining AI and human instruction, could be most effective in enhancing language acquisition among students.
Over a period of thrweeks, a small-scale experiment was conducted to evaluate students' attitudes towards AI chatbots and their influence on speaking skills. The experiment aimed to determine whether using AI-driven tools could enhance language proficiency and confidence in speaking.
For the first two weeks of the experiment, students engaged in daily interactions with two AI platforms: SoulMachine and ChatGPT. These platforms were selected for their advanced natural language processing capabilities, which can simulate humanlike conversations and provide instant feedback. Students used these AI tools to practice speaking on a variety of topics, with the goal of improving their fluency, vocabulary, and overall speaking confidence.
To evaluate the impact of AI interactions, students' speech was recorded at the start of the experiment to establish a baseline for their speaking skills, including fluency, vocabulary, pronunciation, and confidence. After thrweeks, their speech was recorded again to identify changes.
Preliminary results indicate that AI chatbots positively impacted students' speaking skills, notably improving vocabulary and boosting confidence. This was due to frequent practice and real-time feedback from the AI tools.
However, multiple factors, such as students' prior knowledge and individual learning styles, can influence outcomes. While results are promising, further research is needed to fully understand AI's role in language learning. Future studies could involve a larger, diverse sample, longer AI interaction, and various assessment methods, and explore combining AI with traditional teaching methods for a more effective learning environment.
Ultimately, this three-week experiment highlights the potential benefits of using AI chatbots in language learning, particularly in increasing vocabulary and speaking confidence. While the initial results are encouraging, ongoing research and experimentation are essential to fully realize the potential of AI in education and to develop best practices for its implementation.
In conclusion, the reviewed literature underscores the effectiveness of artificial intelligence (AI) in enhancing the communication skills of English language learners. AI technologies provide personalized and adaptive learning experiences, which facilitate the improvement of learners' abilities in speaking, listening, reading, and writing. These technologies offer tailored feedback and practice opportunities, enabling students to progress at their own pace and according to their specific needs.
Despite the clear benefits, ethical considerations and further research are imperative to ensure that AI is used responsibly and equitably in language learning contexts. Issues such as data privacy, algorithmic bias, and the digital divide must be addressed to prevent any unintended negative consequences. Ensuring that AI tools are accessible to all learners, regardless of their socio-economic status, is crucial for maximizing their potential benefits.
By effectively and ethically leveraging AI technologies, educators and policymakers have the opportunity to significantly enhance language learning outcomes. These technologies can empower learners by providing them with the necessary communication skills to succeed in a globally interconnected world. AI-driven language learning tools can bridge gaps in traditional education, offering support and resources that may not be readily available in all educational settings.
Furthermore, ongoing research is needed to refine AI applications in language learning, ensuring they remain effective, unbiased, and aligned with educational goals. Collaboration between technology developers, educators, and policymakers is essential to create AI-driven educational tools that are both innovative and fair. This collaborative effort will help in designing systems that not only improve language proficiency but also foster a more inclusive and equitable educational environment.
СПИСОК ЛИТЕРАТУРЫ:
1. Baker T., Smith L. Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Retrieved from Nesta Foundation website. 2019. P. 115-125;
2. Bandura A. Self-efficacy: The exercise of control. New York: W.H. Freeman. 1997. DOI: 10.4236/jss.2014.211021. 4,488 p;
3. Bonk C.J., Wiley D.A. Preface: Reflections on the waves of emerging learning technologies. Educational Technology Research and Development. 2020. No68 (4). P. 1595-1612;
4. Borkowski J., Carr M., Pressely M. «Spontaneous» strategy use: Perspectives from metacognitive theory. Intelligence. 1987. No11. P. 61-75;
5. Center for American Progress. Future of testing in education: Artificial intelligence. Retrieved August 29, 2022, from https://w.americanprogress.org/article/future-testing-education-artificialintelligence/. 2022;
6. Cope B., Kalantzis M., Searsmith D. Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory. 2020. P. 1-17;
7. Cukurova M., Luckin R. Measuring the impact of emerging technologies in education: A pragmatic approach. Springer, Cham. 2018. P. 25-30;
8. Holmes W., Bialik M., Fadel C. Artificial intelligence in education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign. 2019. P. 43-52;
9. Khanzode K.C.A., Sarode R.D. Advantages and Disadvantages of Artificial Intelligence and Machine Learning: A Literature Review. International Journal of Library & Information Science (IJLIS). 2020. 9(1). P.3;
10. Luckin R., Cukurova M. Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology. 2019. No50 (6). P. 2824-2838;
11. Penkova T.G., Weinstein Y.V. Models and methods of artificial intelligence: A textbook. Krasnoyarsk: Siberian Federal University. 2019. 116 p;
12. Seufert S., Guggemos J., Sailer M. Technology-related knowledge, skills, and attitudes of pre-and in-service teachers: The current situation and emerging trends. Computers in Human Behavior. 2020. P. 106-552
