Fiammetta Caccavale, Carina L. Gargalo, Krist V. Gernaey, Ulrich Krühne
{"title":"Towards Education 4.0: The role of Large Language Models as virtual tutors in chemical engineering","authors":"Fiammetta Caccavale, Carina L. Gargalo, Krist V. Gernaey, Ulrich Krühne","doi":"10.1016/j.ece.2024.07.002","DOIUrl":null,"url":null,"abstract":"<div><p>Recent years have seen the rise of Artificial Intelligence (AI) powered generative chatbots, such as OpenAI’s ChatGPT or Microsoft’s Copilot. These tools have simultaneously positively surprised and taken aback people worldwide, raising the question of whether they can or should be used in education, as well as how to properly guide students and teachers on using them safely and ethically. To this end, this work provides (i) a brief overview of the current applications of AI in Higher Education (HE), (ii) a discussion of the ethical and societal concerns associated with the usage of AI models, (iii) the initial steps of the implementation of a chatbot used at the Technical University of Denmark (DTU) able to perform audits for Good Manufacturing Practice (GMP), and (iv) an investigation of the need and opportunities of AI in chemical engineering education. The latter is achieved through quantitative and qualitative analyses of the responses given by both Master’s students and academia/industry practitioners on the introduction and use of AI in education. This paves the way for discussing current perceptions, expectations and concerns of AI models, as well as their limitations and the opportunities they could provide.</p></div>","PeriodicalId":48509,"journal":{"name":"Education for Chemical Engineers","volume":"49 ","pages":"Pages 1-11"},"PeriodicalIF":3.5000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1749772824000198/pdfft?md5=f50414df641fe41deb31f389c5c26ace&pid=1-s2.0-S1749772824000198-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Education for Chemical Engineers","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1749772824000198","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
Abstract
Recent years have seen the rise of Artificial Intelligence (AI) powered generative chatbots, such as OpenAI’s ChatGPT or Microsoft’s Copilot. These tools have simultaneously positively surprised and taken aback people worldwide, raising the question of whether they can or should be used in education, as well as how to properly guide students and teachers on using them safely and ethically. To this end, this work provides (i) a brief overview of the current applications of AI in Higher Education (HE), (ii) a discussion of the ethical and societal concerns associated with the usage of AI models, (iii) the initial steps of the implementation of a chatbot used at the Technical University of Denmark (DTU) able to perform audits for Good Manufacturing Practice (GMP), and (iv) an investigation of the need and opportunities of AI in chemical engineering education. The latter is achieved through quantitative and qualitative analyses of the responses given by both Master’s students and academia/industry practitioners on the introduction and use of AI in education. This paves the way for discussing current perceptions, expectations and concerns of AI models, as well as their limitations and the opportunities they could provide.
期刊介绍:
Education for Chemical Engineers was launched in 2006 with a remit to publisheducation research papers, resource reviews and teaching and learning notes. ECE is targeted at chemical engineering academics and educators, discussing the ongoingchanges and development in chemical engineering education. This international title publishes papers from around the world, creating a global network of chemical engineering academics. Papers demonstrating how educational research results can be applied to chemical engineering education are particularly welcome, as are the accounts of research work that brings new perspectives to established principles, highlighting unsolved problems or indicating direction for future research relevant to chemical engineering education. Core topic areas: -Assessment- Accreditation- Curriculum development and transformation- Design- Diversity- Distance education-- E-learning Entrepreneurship programs- Industry-academic linkages- Benchmarking- Lifelong learning- Multidisciplinary programs- Outreach from kindergarten to high school programs- Student recruitment and retention and transition programs- New technology- Problem-based learning- Social responsibility and professionalism- Teamwork- Web-based learning