How to Design and Deliver Courses for Higher Education in the AI Era: Insights from Exam Data Analysis

In this position paper, we advocate for the idea that courses and exams in the AI era have to be designed based on two factors: (1) the strengths and limitations of AI, and (2) the pedagogical educational objectives. Based on insights from the Delors report on education [1], we first address the role of education and recall the main objectives that educational institutes must strive to achieve independently of any technology. We then explore the strengths and limitations of AI, based on current advances in AI. We explain how courses and exams can be designed based on these strengths and limitations of AI, providing different examples in the IT, English, and Art domains. We show how we adopted a pedagogical approach that is inspired from the Socratic teaching method from January 2023 to May 2023. Then, we present the data analysis results of seven ChatGPT-authorized exams conducted between December 2022 and March 2023. Our exam data results show that there is no correlation between students' grades and whether or not they use ChatGPT to answer their exam questions. Finally, we present a new exam system that allows us to apply our pedagogical approach in the AI era

ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models

AI generated content (AIGC) presents considerable challenge to educators around the world. Instructors need to be able to detect such text generated by large language models, either with the naked eye or with the help of some tools. There is also growing need to understand the lexical, syntactic and stylistic features of AIGC. To address these challenges in English language teaching, we first present ArguGPT, a balanced corpus of 4,038 argumentative essays generated by 7 GPT models in response to essay prompts from three sources: (1) in-class or homework exercises, (2) TOEFL and (3) GRE writing tasks. Machine-generated texts are paired with roughly equal number of human-written essays with three score levels matched in essay prompts. We then hire English instructors to distinguish machine essays from human ones. Results show that when first exposed to machine-generated essays, the instructors only have an accuracy of 61% in detecting them. But the number rises to 67% after one round of minimal self-training. Next, we perform linguistic analyses of these essays, which show that machines produce sentences with more complex syntactic structures while human essays tend to be lexically more complex. Finally, we test existing AIGC detectors and build our own detectors using SVMs and RoBERTa. Results suggest that a RoBERTa fine-tuned with the training set of ArguGPT achieves above 90% accuracy in both essay- and sentence-level classification. To the best of our knowledge, this is the first comprehensive analysis of argumentative essays produced by generative large language models. Machine-authored essays in ArguGPT and our models will be made publicly available at https://github.com/huhailinguist/ArguGP

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

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

ChatGPT and Works Scholarly: Best Practices and Legal Pitfalls in Writing with AI

Recent advances in artificial intelligence (AI) have raised questions about whether the use of AI is appropriate and legal in various professional contexts. Here, we present a perspective on how scholars may approach writing in conjunction with AI and offer approaches to evaluating whether or not such AI-writing violates copyright or falls within the safe harbor of fair use. We present a set of best practices for standard of care with regard to plagiarism, copyright, and fair use. As AI is likely to grow more capable in the coming years, it is appropriate to begin integrating AI into scholarly writing activities. We offer a framework for establishing sound legal and scholarly foundations

Англійська мова для навчання і работи. Навчальний посібник з англійської мови за професійним спрямуванням для студентів і фахівців галузі знань 0503 Розробка корисних копалин Т 1

A coursebook includes all the activities of students’ work at ESP course aimed at development of language behaviour necessary for effective communication of students in their study and specialism areas. The tasks and activities given in the coursebook are typicalfor students’ academic and professional domains and situations. The content is organized in modules that covers generic job-related language skills of engineers. The authentic texts taken from real life contain interesting up-to-date information about mining, peculiarities of study abroad, customs and traditions of English-speaking countries. Pack of self-study resources given in Part II contains Glossary of mining terms, tasks and activities aimed at developing a range of vocabulary necessary for mining, different functions and functional exponents to be used in academic and professional environment as well as tasks developing self-awareness, self-assessment and self-organisation skills. Testing points for different grammar structuresare given in Part III. Indices at the end of each part easify the use of the coursebook. The coursebook contains illustrations, various samples of visualizing technical information. The coursebook is designed for ESP students of non-linguistic universities. It can be used as teaching/learning materials for ESP Courses for Mining Engineers as well as for self-study of subject and specialist teachers, practicing mining engineers and researchers in Engineering.У посібнику представлені всі види діяльності студентів з вивчення англійської мови, спрямовані на розвиток мовної поведінки, необхідної для ефективного спілкування в академічному та професійному середовищах. Навчальний посібник містить завдання і вправи, типові для різноманітних академічних та професійних сфер і ситуацій. Структура організації змісту– модульна і охоплює загальні мовленнєві вміння інженерів. Зразки текстів– автентичні, взяті з реального життя, містять цікаву та актуальну інформацію про видобувничу промисловість, особливості навчання за кордоном, традиції та звичаї країн, мова яких вивчається. Ресурси для самостійної роботи(Том ІІ) містять глосарій термінів, завдання та вправи для розвитку словарного запасу та розширення діапазону функціональних зразків, необхідних для виконання певних функцій, та завдання, які спрямовані на розвиток навичок самооцінювання і організації свого навчання. Граматичні явища і вправи для їх засвоєння наводяться в томі ІІІ. Наприкінці кожної частини наведено алфавітно-предметні покажчики. Багато ілюстрацій та різних візуальних засобів подання інформації. Навчальний посібник призначений для студентів технічних університетів гірничого профілю. Може використовуватися для самостійного вивчення англійської мови викладачами, фахівцями і науковцями різних інженерних галузей

Simulating the Machine Translation of Low-Resource Languages by Designing a Translator Between English and an Artificially Constructed Language

Natural language processing (NLP), or the use of computers to analyze natural language, is a field that relies heavily on syntax. It would seem intuitive that computers would thrive in this area due to their strict syntax requirements, but the syntax of natural languages leaves them unable to properly parse and generate sentences that seem normal to the average speaker. A subfield of NLP, machine translation, works mainly to computerize translation between different languages. Unfortunately, such translation is not without its weaknesses; language documentation is not created equal, and many low-resource languages—languages with relatively few kinds of documentation, most often written—are left with no way to effectively benefit from machine translation. As a step toward better translation processors for low-resource languages, this thesis examined the possibility of machine translation between high resource languages and low resource languages through an analysis of different machine learning techniques, and ultimately constructing a simple translator between English and an artificially constructed language using a context-free grammar (CFG)

Beyond the design of automated writing evaluation: Pedagogical practices and perceived learning effectiveness in EFL writing classes

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

Bad Ideas About Writing

Bad Ideas About Writing counters major myths about writing instruction. Inspired by the provocative science- and social-science-focused book This Idea Must Die and written for a general audience, the collection offers opinionated, research-based statements intended to spark debate and to offer a better way of teaching writing. Contributors, as scholars of rhetoric and composition, provide a snapshot of major myths about writing instruction in these essays. This collection is published in whole by the Digital Publishing Institute and in part by Inside Higher Ed

Bad Ideas About Writing

Bad Ideas About Writing counters major myths about writing instruction. Inspired by the provocative science- and social-science-focused book This Idea Must Die and written for a general audience, the collection offers opinionated, research-based statements intended to spark debate and to offer a better way of teaching writing. Contributors, as scholars of rhetoric and composition, provide a snapshot of and antidotes to major myths in writing instruction. This collection is published in whole by the Digital Publishing Institute at WVU Libraries and in part by Inside Higher Ed. Supplemental files feature archived episodes of the Bad Ideas About Writing Podcast, read by Dr. Kyle Stedman of Rockford University.https://researchrepository.wvu.edu/dpi-textbooks/1000/thumbnail.jp