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The Evaluation of a Blended Teaching Mode Based on an AI Language Learning Platform
... In most studies, learners reported a positive attitude toward learning EFL with the help of AI. For example, Aljohani (2021), Arini et al. (2022), and Li and Peng (2021) are three studies that reported that learners had a positive attitude toward AI-integrated EFL learning and perceived it as an opportunity to improve their English competence. ...
... The AI tools performed three roles in EFL classes in higher education: as teaching assistants, personal tutors, and learning partners. The chatbots that operated as teaching assistants provided academic knowledge ( [41,[48][49][50]), offered formative feedback to EFL learners [45], and enabled scaffolded learning ( [22,27,31,42,51]). Chatbots also acted as learning partners by chatting and interacting with learners through voice ( [26,28,52,47,53]) and text ( [27,41]). ...
... In language learning, chatbots also have the potential to reduce learning anxiety ( [28,46,47,49]). However, in a study conducted by El Shazly (2021), interactions with a chatbot did not reduce learners' anxiety; it only resulted in a significant improvement in linguistic gains. ...
Integrating Artificial Intelligence (AI) applications into language learning and teaching is currently a growing trend in higher education. Literature reviews have demonstrated the effectiveness of AI applications in improving English as a foreign language (EFL) and English as a second language (ESL) learners' receptive and productive skills, vocabulary knowledge, and intercultural competencies. However, systematic reviews investigating the usefulness of AI technologies in higher education to enhance EFL learners' affective factors are scarce. This study is a systematic review that investigates the effectiveness of integrating AI technologies to enhance EFL learners' motivation, engagement, and attitude, and reduce their learning anxiety. Articles from reputable journal databases such as IEEE, Wiley, Web of Science, Sage, ProQuest, Springer, and Science Direct were screened by examining titles and abstracts, and irrelevant articles were excluded from the search. Of the 64 articles analyzed only 21 articles published between 2017 and 2023 were determined to be relevant to the research topic. The findings suggest that the implementation of AI technologies in EFL contexts is in its early stages, and further research is required to establish the impact of AI-integrated classes on EFL learners’ affective factors. This review also identifies the gaps in literature and recommends avenues for future research in this novel area.
... The IPMLL allows learners to benefit from the interactive and engaging features of CALL, such as multimedia materials and interactive exercises, while also leverages the personalized instruction and adaptive feedback provided by AIALL (Wang & Xu, 2023). Blending CALL and AIALL provides a flexible and dynamic learning environment that caters to learners' individual needs and preferences (Li & Peng, 2021;Wu et al., 2023). By incorporating AIALL technologies into CALL environments, learners can engage in computer-mediated communication, interact with peers and native speakers, and access authentic materials that reflect real-life language use and cultural contexts. ...
Given the great potential of integrating Computer-Assisted Language Learning (CALL) and Artificial Intelligence-Assisted Language Learning (AIALL) to enhance language learning outcomes, there is a growing interest in exploring their combined effects. In this vein, the present study aimed to develop and test an interactive pedagogical model of language learning (IPMLL) by integrating CALL and AIALL elements in a combined module. To further investigate the effects of this model, a comprehensive evaluation was conducted, considering various aspects such as learner motivation, personalized learning experiences, and feedback effectiveness. The results indicate that (1) the integration of CALL and AIALL in the IPMLL positively influenced learner motivation, leading to greater involvement and active participation in language learning activities; (2) the personal learning interactions facilitated by the IPMLL, including adaptive instruction and intelligent feedback, contributed to improved language proficiency and learner satisfaction. Theoretically, this integration aligns with established pedagogical theories and frameworks, such as cognitive theories of multimedia learning, emphasizing the significance of interactive and technology-enhanced learning environments. Pedagogically, the IPMLL offers practical implications for teachers, highlighting the benefits of incorporating CALL and AIALL elements in language teaching methodologies. This study contributes to the growing body of research on technology-enhanced language learning and provides insights for future developments in this field.
... Using transfer learning approaches for NLP tasks, gives a paper on "Exploration and Reflection of Foreign Language Teaching" using "Artificial Intelligence + Education" in the Big Data Era [13]. This study explores the integration of artificial intelligence (AI) with teaching methodologies, specifically in the context of teaching foreign languages. ...
Bully Scan, an artificial intelligence system for identifying offensive language on social media, is proposed in "A Natural Language Processing and Machine Learning-Based Framework to Automatically Identify Cyberbullying. This paradigm, which aims to reduce the negative impacts of cyberbullying and encourage healthy online interactions, is a critical step in using AI for social well-being. The paper, "Research and Practice of Hybrid Teaching Based on AI technology for Foreign Language Translation," offers a novel strategy for teaching foreign languages through the incorporation of AI. The project investigates a hybrid teaching approach that combines AI-powered language translation tools with conventional classroom training. This method seeks to improve accuracy and efficiency of language learning by providing real-time translation support. Through the use of AI technologies, such as machine learning and natural language processing, the system offers students helpful translation assistance, enhancing their educational experience. The study demonstrates encouraging outcomes in terms of raising students' proficiency and effectiveness in translation in a blended learning setting. The paper "Modular Design of English Pronunciation Level Evaluation System Based on Deep Learning Algorithm" offers a novel method for determining pronunciation levels in English by utilizing deep learning algorithms. The study uses techniques like support vector machines and BP neural networks to address the problem of computational intensity in language teaching technologies. Through the application of machine deep learning, the system seeks to improve the precision and efficacy of pronunciation level assessments, providing insightful information for the development of theories for foreign language instruction in the rapidly changing field of artificial intelligence. The study's modular design approach offers a viable foundation for enhancing pronunciation assessment in language instruction.
This paper on artificial intelligence in education (AIEd) has two aims. The first: to explain to a non-specialist, interested, reader what AIEd is: its goals, how it is built, and how it works. The second: to set out the argument for what AIEd can offer teaching and learning, both now and in the future, with an eye towards improving learning and life outcomes for all. Computer systems that are artificially intelligent interact with the world using capabilities (such as speech recognition) and intelligent behaviours (such as using available information to take the most sensible actions toward a stated goal) that we would think of as essentially human. At the heart of artificial intelligence in education is the scientific goal to make knowledge, which is often left implicit, computationally precise and explicit. In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also designed to be a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us more fine-grained understandings of how learning actually happens. Although some might find the concept of AIEd alienating, the algorithms and models that underpin ed tech powered by AIEd form the basis of an essentially human endeavor. Using AIEd, teachers will be able to offer learners educational experiences that are more personalised, flexible, inclusive and engaging. Crucially, we do not see a future in which AIEd replaces teachers. What we do see is a future in which the extraordinary expertise of teachers is better leveraged and augmented through the thoughtful deployment of well designed AIEd. We have available, right now, AIEd tools that could support student learning at a scale previously unimaginable by providing one-on-one tutoring to every student, in every subject. Existing technologies also have the capacity to provide intelligent support to learners working in a group, and to create authentic virtual learning environments where students have the right support, at the right time, to tackle real-life problems and puzzles. In the near future, we expect that teaching and learning will increasingly be supported by the thoughtful application of AIEd tools. For example, by lifelong learning companions powered by AI that can accompany and support individual learners throughout their studies - in and beyond school - and new forms of assessment that measure learning while it is taking place, shaping the learning experience in real time. If we are ultimately successful, we predict that AIEd will help us address some of the most intractable problems in education, including achievement gaps and teacher retention. AIEd will also help us respond to the most significant social challenge that AI has already brought - the steady replacement of jobs and occupations with clever algorithms and robots. It is our view that this provides a new innovation imperative in education, which can be expressed simply: as humans live and work alongside increasingly smart machines, our education systems will need to achieve at levels that none have managed to date. True progress will require the development of an AIEd infrastructure. This will not, however, be a single monolithic AIEd system. Instead, it will resemble the marketplace that has developed for smartphone apps: hundreds and then thousands of individual AIEd components, developed in collaboration with educators, conformed to uniform international data standards, and shared with researchers and developers worldwide. These standards will also enable system-level data collation and analysis that will help us to learn much more about learning itself – and how to improve it. Moving forward, we will need to pay close attention to three powerful forces as we map the future of artificial intelligence in education, namely pedagogy, technology, and system change. Paying attention to the pedagogy will mean that the design of new edtech should always start with what we know about learning. It also means that the system for funding this work must be simultaneously opened up and refocused, moving away from isolated pockets of R&D and toward collaborative enterprises that prioritise areas known to make a real difference to teaching and learning. Paying attention to the technology will mean creating smarter demand for commercial grade AIEd products that work. It also means the development of a robust, component-based AIEd infrastructure, similar to the smartphone app marketplace, where researchers and developers can access standardised components that have been developed in collaboration with educators. Paying attention to system change will mean involving teachers, students, and parents in co-designing new tools, so that AIEd will appropriately address the inherent “messiness” of real classroom, university, and workplace learning environments. It also means the development of data standards that promote the safe and ethical use of data. Said succinctly, we need intelligent technologies that embody what we know about great teaching and learning, embodied in enticing consumer grade products, which are then used effectively in real-life settings that combine the best of human and machine. We do not underestimate the new-thinking, inevitable wrong-turns, and effort required to realise these recommendations. However, if we are to properly unleash the intelligence of AIEd, we must do things differently - via new collaborations, sensible funding, and (always) a keen eye on the pedagogy. The potential prize is too great to act otherwise.
While theoretical approaches to error correction vary in the second language acquisition (SLA) literature, most sources agree that such correction is useful and leads to learning. While some point out the relevance of the communicative context in which the correction takes place, others stress the value of consciousness-raising. Trying to reconcile the two approaches, this paper describes an application of artificial intelligence in the second language error remediation process. The software presented is called the Intelligent Tutor. It diagnoses some typical errors in the writing of university students who are learning English as a second language. A quasi-experimental study consisting of a grammaticality judgment pre-test, a treatment in the form of the Intelligent Tutor and a short answer post-test, was carried out with 266 university students in three countries. The findings show that artificial intelligence is an efficient instrument of error remediation, reducing the error rate by an average of 83%. This paper discusses the theoretical underpinnings of the software used, briefly describes the software itself and then presents the study design, its findings and their implications in the wider context of second language learning.
Automated writing evaluation (AWE) software is designed to provide instant computer-generated scores for a submitted essay along with diagnostic feedback. Most studies on AWE have been conducted on psychometric evaluations of its validity; however, studies on how effectively AWE is used in writing classes as a pedagogical tool are limited. This study employs a naturalistic classroom-based approach to explore the interaction between how an AWE program, MY Access!, was implemented in three different ways in three EFL college writing classes in Taiwanand how students perceived its effectiveness in improving writing. The findings show that, although the implementation of AWE was not in general perceived very positively by the three classes, it was perceived comparatively more favorably when the program was used to facilitate students’ early drafting and revising process, followed by human feedback from both the teacher and peers during the later process. This study also reveals that the autonomous use of AWE as a surrogate writing coach with minimal human facilitation caused frustration to students and limited their learning of writing. In addition, teachers’ attitudes toward AWE use and their technology-use skills, as well as students’ learner characteristics and goals for learning to write, may also play vital roles in determining the effectiveness of AWE. With limitations inherent in the design of AWE technology, language teachers need to be more critically aware that the implementation of AWE requires well thought-out pedagogical designs and thorough considerations for its relevance to the objectives of the learning of writing.
Automated writing evaluation (AWE) systems have attracted much attention for their efficiency in feedback provision and automated essay scoring in language classrooms. However, teachers’ roles in this technology-enhanced environment and their potential impact on students’ writing development are still under-investigated. To fill this gap, this classroom-based study examined three English as a second language (ESL) teacher’s perceptions and uses of Criterion®, an AWE system developed by Educational Testing Service (ETS), in four ESL writing classes at a large Midwestern U.S. university. Student’s writing performance data, including submission behaviors, revision types, and progress in grammatical accuracy, were then analyzed in light of teachers’ perceptions and reported uses of Criterion. The results indicate that the teachers took different approaches to integrating Criterion in their classes, which, in turn, was reflected in observable differences in students’ essay submission frequencies on Criterion, revision types, and changes in error rates. The findings shed light on the importance of teacher agency and cognition in technology-supported ESL classrooms. Implications for teaching English writing with AWE systems are discussed.
Automated essay scoring (AWE) software, which uses artificial intelligence to evaluate essays and generate feedback, has been seen as both a boon and a bane in the struggle to improve writing instruction. We used interviews, surveys, and classroom observations to study teachers and students using AWE software in 4 secondary schools. We found AWE to be a modest addition to the arsenal of teaching tools and techniques at the teacher's disposal, roughly midway between the fears of some and the hopes of others. The program saved teachers' time and encouraged more revision but did not appear to result in substantially more writing or greater attention to content and organization. Teachers' use of the software varied from school to school, based partly on students' socioeconomic status, but more notably on teachers' prior beliefs about writing pedagogy.
This study examined the construct validity of the e‐rater ® automated essay scoring engine as an alternative to human scoring in the context of TOEFL® essay writing. Analyses were based on a sample of students who repeated the TOEFL within a short time period. Two e‐rater scores were investigated in this study, the first based on optimally predicting the human essay score and the second based on equal weights for the different features of e‐rater.
Within a multitrait‐multimethod approach, the correlations and reliabilities of human and e‐rater scores were analyzed together with TOEFL subscores (structured writing, reading, and listening) and with essay length. Possible biases between human and e‐rater scores were examined with respect to differences in performance across countries of origin and differences in difficulty across prompts. Finally, a factor analysis was conducted on the e‐rater features to investigate the interpretability of their internal structure and determine which of the two e‐rater scores reflects this structure more closely.
Results showed that the e‐rater score based on optimally predicting the human score measures essentially the same construct as human‐based essay scores with significantly higher reliability and consequently higher correlations with related language scores. The equal‐weights e‐rater score showed the same high reliability but significantly lower correlation with essay length. It is also aligned with the 3‐factor hierarchical (word use, grammar, and discourse) structure that was discovered in the factor analysis. Both e‐rater scores also successfully replicate human score differences between countries and prompts.
With the development of Internet technologies, online distance education is becoming an important format for teaching, learning, and instruction. In the 21st century, bleeding edge technologies make it possible for more and more students, especially adults, to benefit from an online education. This new paradigm in education allows students to pursue a second or third degree without sacrificing their jobs. Despite the advances in technology, existing online course management systems are far behind in the technological applications needed to deliver the highest quality online education. The existing distance education systems do not give enough consideration to many important issues, like personalization, mobility of students and instructors, and coordination among online study groups, which seriously affect further development of online education. This article discusses the potential applications of intelligent agents and other modern technologies to solve these problems and make online education more accountable in the open and dynamic environment.
Computer Simulation in Educational Communication (CSIEC) is not only an intelligent web-based human-computer dialogue system with natural language for English instruction but also a learning assessment system for learners and teachers. Its multiple functions-including grammar-based gap-filling exercises, scenario show, free chatting, and chatting on a given topic-can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of the dialogue system, as well as a survey of related works, I will illustrate the system structure and describe its pedagogical functions with the underlying AI techniques, such as natural language processing and rulebased reasoning, in detail. I will summarize the free Internet usage within a six-month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by users, and the application of the CSIEC system on English instruction can motivate learners to practice English and enhance their learning process. Finally, I will conclude with potential improvements. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.
Automated writing evaluation (AWE) software uses artificial intelligence (AI) to score student essays and support revision. We studied how an AWE program called MY Access!® was used in eight middle schools in Southern California over a three-year period. Although many teachers and students considered automated scoring unreliable, and teachers’ use of AWE was limited by the desire to use conventional writing methods, use of the software still brought important benefits. Observations, interviews, and a survey indicated that using AWE simplified classroom management and increased students’ motivation to write and revise.
The use of feedback in the ESL writing class integrating automated essay scoring (AES)
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Research on the Blended Teaching based on Longman Interactive English Learning Platformr——Taking Hubei University of Education as an Example
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A study on the MOOCs-based blended intercultural foreign language teaching
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