The application of chatbot as an L2 writing practice tool

This study investigates the effect of chatbot-based writing practices on second language learners’ writing performance and perceptions of using the chatbot in L2 writing practices. A total of 75 Korean elementary school students were randomly allocated to two groups. While the control group received traditional teacher-led writing instruction, the experimental group used a chatbot for individual writing practices for 15 weeks. The chatbot was developed using Google’s Dialogflow machine-learning AI platform by encoding expressions from an elementary school English textbook. A pretest was carried out prior to the experiment to examine the initial writing performance, and a posttest was carried out 15 weeks later with a different writing topic. The participants in the experimental group also responded to a short survey to report their perceptions and opinions about the chatbot. The results showed that the two groups generally showed a similar writing proficiency in the pretest scores, but the experimental group performed significantly better in the posttest than the control group, suggesting that the chatbot-based writing practice had a facilitating effect on their test performance. The participants of the experimental group also found the chatbot useful in improving their language skills and made them feel comfortable when learning a foreign language

Evaluating intelligent personal assistants for L2 listening and speaking development

While the use of intelligent personal assistants (IPAs) has exploded in recent years, little is known about their use to promote English as a foreign language (EFL) development. Thus, this study addresses this gap in the literature by examining the in-class use of the IPA, Alexa, among second language (L2) English students to support improvements in listening comprehension and speaking proficiency. The study utilized a quasi-experimental design with an experimental group (n = 13) which took part in a 10-week treatment of student-IPA interaction and a control group (n = 15) which did not. Results from the Mann-Whitney U test found that the experimental group was able to make more significant gains in L2 speaking proficiency. However, a significant difference was not found when comparing improvements in L2 listening comprehension. These findings suggest that IPAs may be a useful tool to promote L2 speaking skills and underscore the necessity for additional research on the emerging technology for language learning

Robust Dialog Management Through A Context-centric Architecture

This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by ContextBased Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user’s assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users

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

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education

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

Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning

IntroductionThis mixed methods study examines the effects of AI-mediated language instruction on English learning achievement, L2 motivation, and self-regulated learning among English as a Foreign Language (EFL) learners. It addresses the increasing interest in AI-driven educational technologies and their potential to revolutionize language instruction.MethodsTwo intact classes, consisting of a total of 60 university students, participated in this study. The experimental group received AI-mediated instruction, while the control group received traditional language instruction. Pre-tests and post-tests were administered to evaluate English learning achievement across various domains, including grammar, vocabulary, reading comprehension, and writing skills. Additionally, self-report questionnaires were employed to assess L2 motivation and self-regulated learning.ResultsQuantitative analysis revealed that the experimental group achieved significantly higher English learning outcomes in all assessed areas compared to the control group. Furthermore, they exhibited greater L2 motivation and more extensive utilization of self-regulated learning strategies. These results suggest that AI-mediated instruction positively impacts English learning achievement, L2 motivation, and self-regulated learning.DiscussionQualitative analysis of semi-structured interviews with 14 students from the experimental group shed light on the transformative effects of the AI platform. It was found to enhance engagement and offer personalized learning experiences, ultimately boosting motivation and fostering self-regulated learning. These findings emphasize the potential of AI-mediated language instruction to improve language learning outcomes, motivate learners, and promote autonomy.ConclusionThis study contributes to evidence-based language pedagogy, offering valuable insights to educators and researchers interested in incorporating AI-powered platforms into language classrooms. The results support the notion that AI-mediated language instruction holds promise in revolutionizing language learning, and it highlights the positive impact of AI-driven educational technologies in the realm of language education

Robots, Cyborgs, and Humans. A Model of Consumer Behavior in Services: A Study in the Healthcare Services Sector

La present tesi es basa en una investigació que proposa un ús futurista de l'robot i el cyborg com cirurgians oculars. El model desenvolupat investiga la intenció de l'consumidor per elegir cada cirurgià (és a dir: cirurgià robot, cirurgià cyborg o cirurgià humà). Les dades es van analitzar utilitzant la tècnica PLS-SEM. Els resultats de la investigació mostren que l'expectativa d'esforç, l'expectativa de rendiment, el risc percebut i la influència social van mostrar un impacte significatiu en la intenció d'utilitzar els serveis de l'robot cirurgià. Els resultats de el model per al cyborg cirurgià van confirmar l'impacte significatiu de l'expectativa d'esforç, l'excitació, l'expectativa de rendiment i la influència social en la intenció d'utilitzar els seus serveis. L'expectativa d'esforç i la influència social van confirmar un impacte significatiu en la intenció d'utilitzar els serveis de l'cirurgià humà. Els resultats mostren que en els tres models les variables influència social i expectativa d'esforç afecten significativament a la intenció d'utilitzar aquests serveis de cirurgia i que amb diferent intensitat entre els models per expectativa de esforç-. L'impacte de la influència social dóna una idea general sobre la naturalesa de el sector de la salut a Jordània, on una part de la societat presta més atenció a les recomanacions dels altres a l'elegir els seus cirurgians. A més, l'impacte de l'expectativa d'esforç contribueix a les expectatives per la simplicitat de l'servei dels pacients, en termes d'ús i interacció amb els cirurgians proposats. L'anàlisi multigrup va confirmar que les variables dels models estan afectant de la mateixa manera a l'comparar la intenció d'usar cyborgs i humans, i a l'comparar cyborgs i robots. No obstant això, sí que hi ha diferències significatives a l'comparar l'elecció entre robots i humans en l'impacte de l'expectativa d'esforç per utilitzar els serveis de cirurgia. D'altra banda, els participants van mostrar la seva preferència pel cirurgià humà sobre els cirurgians cyborg i robot, respectivament. Com a resultat, l'acceptació de les tecnologies de robot i cyborg per part de la societat podria donar una idea sobre la lluita esperada en el futur entre el desenvolupament de robots i la millora de les capacitats humanes.La presente tesis se basa en una investigación que propone un uso futurista del robot y el cyborg como cirujanos oculares. El modelo desarrollado investiga la intención del consumidor para elegir a cada cirujano (es decir: cirujano robot, cirujano cyborg o cirujano humano). Los datos se analizaron utilizando la técnica PLS-SEM. Los resultados de la investigación muestran que la expectativa de esfuerzo, la expectativa de rendimiento, el riesgo percibido y la influencia social mostraron un impacto significativo en la intención de utilizar los servicios del robot cirujano. Los resultados del modelo para el cyborg cirujano confirmaron el impacto significativo de la expectativa de esfuerzo, la excitación, la expectativa de rendimiento y la influencia social en la intención de usar sus servicios. La expectativa de esfuerzo y la influencia social confirmaron un impacto significativo en la intención de utilizar los servicios del cirujano humano. Los resultados muestran que en los tres modelos las variables influencia social y expectativa de esfuerzo afectan significativamente a la intención de usar esos servicios de cirugía –aunque con distinta intensidad entre los modelos para expectativa de esfuerzo-. El impacto de la influencia social da una idea general sobre la naturaleza del sector de la salud en Jordania, donde una parte de la sociedad presta más atención a las recomendaciones de los demás al elegir a sus cirujanos. Además, el impacto de la expectativa de esfuerzo contribuye a las expectativas por la simplicidad del servicio de los pacientes, en términos de uso e interacción con los cirujanos propuestos. El análisis multigrupo confirmó que las variables de los modelos están afectando de la misma manera al comparar la intención de usar cyborgs y humanos, y al comparar cyborgs y robots. Sin embargo, sí que existen diferencias significativas al comparar la elección entre robots y humanos en el impacto de la expectativa de esfuerzo para utilizar los servicios de cirugía. Por otro lado, los participantes mostraron su preferencia por el cirujano humano sobre los cirujanos cyborg y robot, respectivamente. Como resultado, la aceptación de las tecnologías de robot y cyborg por parte de la sociedad podría dar una idea sobre la lucha esperada en el futuro entre el desarrollo de robots y la mejora de las capacidades humanThe research proposes a futuristic use of robot and cyborg as surgeons in an eye surgery. Thereafter, the developed model has been applied to investigate the intention to use each surgeon (i.e. robot surgeon, cyborg surgeon, and human surgeon). The data was analyzed using the PLS-SEM technique. According to the research results, effort expectancy, performance expectancy, perceived risk, and social influence showed a significant impact on intention to use robot services. However, the results of the cyborg service model confirmed the significant impact of effort expectancy, arousal, performance expectancy, and social influence on the intention to use cyborg services. Furthermore, effort expectancy and social influence confirmed their significant impact on the intention to use human services. The results of the three models showed that the variables social influence and effort expectancy significantly affected the intention to use these surgical services, with a different intensity between the models for effort expectancy. The social influence impact gives a general idea about the nature of the healthcare sector in Jordan, where a part of society gives more attention to the recommendation from others while choosing their surgeons. Also, the effort expectancy impact contributes to patients' expectations of simplicity, in terms of use and interaction with the proposed surgeons. The multigroup analysis confirmed that the models' variables are affecting the intention to use cyborg and human service, and cyborg and robots in the same way. However, the differences were confirmed between robot and human cyborgs in terms of the impact of effort expectancy on the intention to use these services. On the other side, the participants showed their preference of the human surgeon over the cyborg and robot surgeons, respectively. As a result, the acceptance of the robot and cyborg technologies by a part of the society could give an idea about the expected struggle in the future among developing robots and enhancing human capabilities

The dawn of the human-machine era: a forecast of new and emerging language technologies

New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world's smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawn