Artificial Intelligence and English Classroom: The Implications of AI Toward EFL Students’ Motivation

The integration of Artificial Intelligence (AI) into the realm of English as a Foreign Language (EFL) education has brought forth transformative possibilities, particularly in the context of student motivation.  This research employs a mixed-methods approach, encompassing quantitative surveys and qualitative study of previous literature, to delve into the multifaceted dimensions of EFL students' motivation within the AI-driven learning environment. Quantitative data collection involves administering structured survey questionnaires to a diverse group of EFL students who have experienced AI-based language learning tools. Qualitative insights are gleaned through document/literature study, aiming to provide in-depth narratives and understandings concerning the participants' experiences, attitudes, and perceptions regarding AI in EFL classroom. This integration highlights the nuanced interplay between AI technologies and motivation factors, shedding light on how AI can enhance intrinsic motivation, boost self-efficacy, and facilitate personalized language learning experiences. Moreover, the study addresses ethical considerations, ensuring the privacy and well-being of participants throughout the research process. Ultimately, the research contributes to the growing body of knowledge on AI's role in education and its potential to shape the future of EFL instruction. By unveiling the implications of AI on EFL students' motivation, this study provides valuable insights for educators, policymakers, and stakeholders, guiding the effective integration of AI to foster enduring and enthusiastic language learners in an increasingly digital age

한국인 고등학생의 영어 형용사 타동결과구문 학습에서의 인공지능 챗봇 기반 교수의 효과

학위논문(박사) -- 서울대학교대학원 : 사범대학 외국어교육과(영어전공), 2022.2. 김기택.English adjectival transitive resultative constructions (VtR) are notoriously challenging for Korean L2 English learners due to their syntactic and semantic differences from their L1 counterparts. To deal with such a complex structure, like English adjectival VtR, Korean L2 English learners need instructional interventions, including explicit instructions and corrective feedback on the target structure. Human instructors are virtually incapable of offering adequate corrective feedback, as providing corrective feedback from a human teacher to hundreds of students requires excessive time and effort. To deal with the practicality problems faced by human instructors in providing corrective feedback, numerous artificial intelligence (AI) chatbots have been developed to provide foreign language learners with corrective feedback on par with human teachers. Regrettably, many currently available AI chatbots remain underdeveloped. In addition, no prior research has been conducted to assess the effectiveness of corrective feedback offered by an AI chatbot, a human instructor, or additional explicit instruction via video material. The current study examined the instructional effects of corrective feedback from an AI chatbot on Korean high school students’ comprehension and production of adjectival VtR. Also, the current study investigated whether the corrective feedback generated by the AI chatbot enables Korean L2 English learners to expand their constructional repertoire beyond instructed adjectival VtR to uninstructed prepositional VtR. To investigate these issues, text-based Facebook Messenger AI chatbots were developed by the researcher. The effectiveness of the AI chatbots’ corrective feedback was compared with that of a human instructor and with additional video material. Students were divided into four groups: three instructional groups and one control group. The instructional groups included a chatbot group, a human group, and a video group. All learners in the three instructional groups watched a 5-minute explicit instruction video on the form and meaning pairings of the adjectival VtR in English. After that, learners were divided into three groups based on their preferences for instructional types. The learners volunteered to participate in the instructional procedures with corrective feedback from a text-based AI chatbot, a human instructor, or additional explicit instruction using a 15-minute video. Moreover, they took part in three testing sessions, which included a pretest, an immediate posttest, and a delayed posttest. The control group students were not instructed, and only participated in the three testing sessions. Two tasks were used for each test session: an acceptability judgment task (AJT) and an elicited writing task (EWT). The AJT tested participants’ comprehension of instructed adjectival VtR and uninstructed prepositional VtR. The EWT examined the correct production of instructed adjectival VtR and uninstructed prepositional VtR. The results of the AJT revealed that the instructional treatment (e.g., corrective feedback from the AI chatbot or a human instructor, or additional explicit instruction from the video material) was marginally more effective at improving the comprehension of adjectival VtR than was the case with the control group. On the other hand, the instructional treatment on the adjectival VtR failed in the generalization to prepositional VtR which was not overtly instructed. In the EWT, the participants in the corrective feedback groups (e.g., the chatbot and human groups) showed a more significant increase in the correct production of the instructed adjectival VtR more so than those in the video and control groups. Furthermore, the chatbot group learners showed significantly higher production of uninstructed prepositional VtR compared to any other group participants. These findings suggest that chatbot-based instruction can help Korean high school L2 English learners comprehend and produce complex linguistic structures—namely, adjectival and prepositional VtR. Moreover, the current study has major pedagogical implications for principled frameworks for implementing AI chatbot-based instruction in the context of foreign language learning.영어 형용사 타동결과구문(English Adjectival Transitive Resultative Construction)은 한국인 영어 학습자들에게 모국어의 대응 구문이 갖는 의미 통사론적 차이로 인해 학습하기 매우 어려운 것으로 알려져 있다. 따라서 영어 형용사 타동결과구문과 같은 복잡한 구문을 학습하기 위해서, 한국인 영어 학습자들에게는 목표 구조에 대한 명시적 교수와 교정적 피드백을 포함한 교수 처치가 요구된다. 수백 명의 학습자들에게 교정적 피드백을 제공하기 위해서는 과도한 시간과 노력이 요구되기 때문에, 인간 교사가 적절한 양의 교정적 피드백을 제공한다는 것은 사실상 불가능하다. 교정적 피드백을 제공할 때 직면하는 이러한 실용성 문제를 해결하기 위하여, 외국어 학습자들에게 인간 교사와 유사한 교정 피드백을 제공할 수 있는 수많은 인공 지능(AI) 챗봇이 개발되었다. 유감스럽게도, 현재 사용 가능한 많은 외국어 학습용 인공지능 챗봇은 아직 충분히 개발되지 않은 상태에 남아있으며, 인공지능 챗봇의 교정적 피드백이 갖는 교수효과를 비교 분석한 연구는 현재 이루어지지 않은 상태다. 이러한 선행연구의 한계에 초점을 두어, 본 연구에서는 인공지능 챗봇의 교정적 피드백이 한국 고등학생의 영어 형용사 타동결과구문의 이해와 생성에 미치는 교수 효과를 살펴보았다. 또한 본 연구에서는 이러한 교수 효과가 언어적으로 관련된 다른 영어 구문의 학습에도 영향을 끼치는지를 알아보기 위해 교실에서 직접 가르치지 않았던 구문인 영어 전치사 타동결과구문(English Prepositional Transitive Resultative Construction)의 학습 양상을 알아보았다. 이를 위해, 본 연구에서는 텍스트 메시지 기반의 페이스북 메신저에서 구동되는 인공지능 챗봇을 개발하였다. 인공지능 챗봇의 교수효과 검증을 위해 본 연구에 참여한 학생들은 네 개의 집단으로 구분되었다: 세 개의 교수 집단에는 교수처치가 적용되었고, 한 개의 통제 집단에서는 교수처치가 적용되지 않았다. 교수처치가 적용된 세 개의 집단은 챗봇그룹, 인간그룹, 영상그룹으로 분류되었으며, 이들은 모두 영어로 된 형용사 타동결과구문의 형태와 의미 쌍에 대한 5분 길이의 학습 비디오를 시청함으로써 명시적 교수 처치를 받았다. 또한 비디오를 시청한 후 세 그룹의 학습자들은 교재를 통해 제공되는 언어연습자료를 해결하는 과업에 참여하였다. 다음으로 세 집단(챗봇그룹, 인간그룹, 영상그룹)은 다음과 같은 추가적 교수처치를 받았다: 챗봇그룹 학습자들은 교재 활동과 관련된 텍스트 기반 인공지능 챗봇과의 대화에 참여함으로써 오류에 대한 교정적 피드백을 받았다. 인간그룹 학습자들은 교재활동을 완수한 내용을 인간 교사에게 전송하고, 이에 대한 교정적 피드백을 받았다. 영상그룹 학습자들은 교재활동을 완수한 후 이에 대한 15분의 추가적인 명시적 교수자료를 영상으로 시청하였다. 학습자의 교수효과는 사전시험, 사후시험 및 지연 사후시험으로 검증되었다. 한편 통제 집단 학생들은 교수처치 없이 세 번의 시험에만 참여하였다. 세 차례의 시험에서는 수용성판단과제(AJT)와 유도작문과제(EWT)의 두 가지 과제가 사용되었다. 수용성판단과제를 통하여, 교수된 영어 형용사 타동결과구문과 지시되지 않은 영어 전치사 타동결과구문 대한 참가자의 이해도를 측정하였다. 유도작문과제를 통하여 교수된 영어 형용사 타동결과구문과 지시되지 않은 영어 전치사 타동결과구문을 참여자가 정확하게 산출할 수 있는지를 측정하였다. 시험의 결과는 다음과 같았다. 수용성판단과제의 경우, 교수처치가 적용된 세 집단이 통제 집단보다 형용사 타동결과구문의 이해도 향상에 약간 더 효과적인 것으로 나타났다. 하지만 형용사 타동결과구문에 대한 교수적처치는 교수되지 않은 전치사 타동결과구문으로의 학습에 영향을 주지 못하였다. 유도작문과제의 경우, 인공지능 챗봇이나 인간 교사에 의해 제공되는 교정 피드백 그룹의 참가자가 영상그룹 및 통제집단의 참가자보다 형용사 타동결과구문의 올바른 생성에 더 유의미한 영향을 미치는 것으로 드러났다. 동일한 교수 효과가 전치사 타동결과구문의 학습에서도 관측되어, 형용사 타동결과구문의 학습이 전치사 타동결과구문의 학습에 일반화가 일어났다. 본 연구는 인간 교사가 직면해야 하는 실용성 문제를 극복하고, 인공지능 챗봇이 한국인 고등학교 L2 영어 학습자가 형용사 및 전치사 타동결과구문과 같은 복잡한 언어 구조를 이해하고 생성하는 데에 인간 교사와 비견될 정도로 교정적 피드백을 제공할 수 있을 것임을 시사한다. 또한, 본 연구는 인공지능 챗봇 기반 외국어 교육의 실제적 사례 및 효과를 선도적으로 보여주었다는 점에서 의미가 있다.ABSTRACT i TABLE OF CONTENTS iii LIST OF TABLES v LIST OF FIGURES vii CHAPTER 1. INTRODUCTION 1 1.1. Statement of Problems and Objectives 1 1.2. Scope of the Research 6 1.3. Research Questions 9 1.4. Organization of the Dissertation 10 CHAPTER 2. LITERATURE REVIEW 12 2.1. Syntactic and Semantic Analysis of Korean and English Transitive Resultative Constructions 13 2.1.1. Syntactic Analysis of English Transitive Resultative Construction 13 2.1.2. Syntactic Analysis of Korean Transitive Resultative Constructions 25 2.1.3. Semantic Differences in VtR between Korean and English 46 2.1.4. Previous acquisition study on English adjectival and prepositional VtR 54 2.2. Corrective Feedback 59 2.2.1. Definition of Corrective Feedback 59 2.2.2. Types of Corrective Feedback 61 2.2.3. Noticeability in Corrective Feedback 67 2.2.4. Corrective Recast as a Stepwise Corrective Feedback 69 2.3. The AI Chatbot in Foreign Language Learning 72 2.3.1. Non-communicative Intelligent Computer Assisted Language Learning (ICALL) 73 2.3.2. AI Chatbot without Corrective Feedback 79 2.3.3. AI Chatbot with Corrective Feedback 86 2.4. Summary of the Literature Review 92 CHAPTER 3. METHODOLOGY 98 3.1. Participants 98 3.2. Target Structure 102 3.3. Procedure of the Study 106 3.4. Instructional Material Shared by the Experimental Group 107 3.4.1. General Framework of the Instructional Session 108 3.4.2. Instructional Material Shared by Experimental Groups 111 3.5. Group-specific Instructional Treatments: Post-Written Instructional Material Activities on Corrective Feedback from Chatbot, Human, and Additional Explicit Instruction via Video 121 3.5.1. Corrective Feedback from the AI Chatbot 122 3.5.2. Corrective Feedback from a Human Instructor 136 3.5.3. Additional Instruction via Video Material 139 3.6. Test 142 3.6.1. Acceptability Judgment Task (AJT) 144 3.6.2. Elicited Writing Task (EWT) 150 3.7. Statistical Analysis 152 CHAPTER 4. RESULTS AND DISCUSSIONS 154 4.1. Results of Acceptability Judgment Task (AJT) 154 4.1.1. AJT Results of Instructed Adjectival VtR 155 4.1.2. AJT Results of Uninstructed Prepositional VtR 160 4.1.3. Discussion 164 4.2. Results of Elicited Writing Task (EWT) 175 4.2.1. EWT Results for Instructed Adjectival VtR 176 4.2.2. EWT Results of Uninstructed Prepositional VtR 181 4.2.3. Further Analysis 187 4.2.4. Discussion 199 CHAPTER 5. CONCLUSION 205 5.1. Summary of the Findings and Implications 205 5.2. Limitations and Suggestions for Future Research 213 REFERENCES 217 APPENDICES 246 ABSTRACT IN KOREAN 297박

Supporting learning activities in virtual worlds: methods, tools and evaluation

2011 - 2012Continuing advances and reduced costs in computational power, graphics and network bandwidth let 3D immersive multi‐user Virtual Worlds (VWs) become increasingly accessible while offering an improved and engaging quality of experience. Excited at the prospects of engaging their Net Generation, students and educators worldwide are attempting to exploit the affordances of three‐dimensional (3D) VWs. Environments such as Second Life (SL) are increasingly used in education, often for their flexibility in facilitating student‐directed, self‐paced learning and their communication features. Research on the educational value of VWs has revealed their potential as learning platforms. However, further studies are always needed in order to assess their effectiveness, satisfactorily and social engagement, not only in the general didactic use of the environment, but also for each specific learning subjects, activities and modality. A major question in using VWs in education is finding appropriate value‐added educational applications. The main challenge is to determine learning approaches in which learning in a VW presents added value with respect to traditional education, and to effectively utilize the third dimension to avoid using the environment simply as a communication platform. In addition, the educational VW activities become more and more sophisticated, starting from the early ones based only on information displaying and teaching resources to simulated laboratory and scenarios. The more complex the learning activities are, the more the challenge of guiding students during their learning trajectories increases and there is the need of providing them with appropriate support and guidance. The main contributions of this thesis are summarized as follows: (i) we propose an appropriate value‐added educational application that supports individual learning activities effectively exploiting the third dimension. In particular, we adopt a VW to support the learning of engineering practices based on technical drawing. The proposed system called VirtualHOP trains the students in the way of learning‐by‐doing methodology to build the required 3D objects; (ii) we enhance an helping system with the avatar appearance and AI for helping the exploration of environments and fruition of distance didactic activities in SL; (iii) we empirically evaluate the didactic value and the user perceptions concerning both the learning setting and the avatar‐based virtual assistant. The results demonstrate the usefulness of both the didactic experiences offered in SL and a positive attitude of the learners in terms of enjoyment and ease‐of‐use. [edited by author]XI n.s

Methods for pronunciation assessment in computer aided language learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 149-176).Learning a foreign language is a challenging endeavor that entails acquiring a wide range of new knowledge including words, grammar, gestures, sounds, etc. Mastering these skills all require extensive practice by the learner and opportunities may not always be available. Computer Aided Language Learning (CALL) systems provide non-threatening environments where foreign language skills can be practiced where ever and whenever a student desires. These systems often have several technologies to identify the different types of errors made by a student. This thesis focuses on the problem of identifying mispronunciations made by a foreign language student using a CALL system. We make several assumptions about the nature of the learning activity: it takes place using a dialogue system, it is a task- or game-oriented activity, the student should not be interrupted by the pronunciation feedback system, and that the goal of the feedback system is to identify severe mispronunciations with high reliability. Detecting mispronunciations requires a corpus of speech with human judgements of pronunciation quality. Typical approaches to collecting such a corpus use an expert phonetician to both phonetically transcribe and assign judgements of quality to each phone in a corpus. This is time consuming and expensive. It also places an extra burden on the transcriber. We describe a novel method for obtaining phone level judgements of pronunciation quality by utilizing non-expert, crowd-sourced, word level judgements of pronunciation. Foreign language learners typically exhibit high variation and pronunciation shapes distinct from native speakers that make analysis for mispronunciation difficult. We detail a simple, but effective method for transforming the vowel space of non-native speakers to make mispronunciation detection more robust and accurate. We show that this transformation not only enhances performance on a simple classification task, but also results in distributions that can be better exploited for mispronunciation detection. This transformation of the vowel is exploited to train a mispronunciation detector using a variety of features derived from acoustic model scores and vowel class distributions. We confirm that the transformation technique results in a more robust and accurate identification of mispronunciations than traditional acoustic models.by Mitchell A. Peabody.Ph.D

Modelling a conversational agent (Botocrates) for promoting critical thinking and argumentation skills

Students in higher education institutions are often advised to think critically, yet without being guided to do so. The study investigated the use of a conversational agent (Botocrates) for supporting critical thinking and academic argumentation skills. The overarching research questions were: can a conversational agent support critical thinking and academic argumentation skills? If so, how? The study was carried out in two stages: modelling and evaluating Botocrates' prototype. The prototype was a Wizard-of-Oz system where a human plays Botocrates' role by following a set of instructions and knowledge-base to guide generation of responses. Both stages were conducted at the School of Education at the University of Leeds. In the first stage, the study analysed 13 logs of online seminars in order to define the tasks and dialogue strategies needed to be performed by Botocrates. The study identified two main tasks of Botocrates: providing answers to students' enquiries and engaging students in the argumentation process. Botocrates’ dialogue strategies and contents were built to achieve these two tasks. The novel theoretical framework of the ‘challenge to explain’ process and the notion of the ‘constructive expansion of exchange structure’ were produced during this stage and incorporated into Botocrates’ prototype. The aim of the ‘challenge to explain’ process is to engage users in repeated and constant cycles of reflective thinking processes. The ‘constructive expansion of exchange structure’ is the practical application of the ‘challenge to explain’ process. In the second stage, the study used the Wizard-of-Oz (WOZ) experiments and interviews to evaluate Botocrates’ prototype. 7 students participated in the evaluation stage and each participant was immediately interviewed after chatting with Botocrates. The analysis of the data gathered from the WOZ and interviews showed encouraging results in terms of students’ engagement in the process of argumentation. As a result of the role of ‘critic’ played by Botocrates during the interactions, users actively and positively adopted the roles of explainer, clarifier, and evaluator. However, the results also showed negative experiences that occurred to users during the interaction. Improving Botocrates’ performance and training users could decrease users’ unsuccessful and negative experiences. The study identified the critical success and failure factors related to achieving the tasks of Botocrates