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Global Trends in Scientific Debates on Trustworthy and Ethical Artificial Intelligence and education 1

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25th International conference on Artificial Intelligence in education (AIED)

作者： Stracke, Christian M. Chounta, Irene-Angelica Homes, Wayne Univ Bonn Bonn Germany Univ Duisburg Essen Duisburg Germany UCL London England

ISBN: (数字)9783031643156

ISBN: (纸本)9783031643149;9783031643156

this paper presents a systematic review of the scientific literature on trustworthy and ethical Artificial Intelligence (AI) and education (AI&ED), including both AI applied in education to support teaching and learning (AIED), as well as education about AI (AI literacy). Key interest is the identification of global trends with a special focus on unbalanced disparities. Strictly following the standardised protocol and the underlying PRISMA approach, 324 records were identified and selected according to the pre-defined protocol for the systematic review. Finally, 62 articles were included in the quantitative and qualitative analysis in response to four research questions: Which (i) journals, (ii) disciplines, and (iii) regions are leading scientific debates and sustainable developments in education and trustworthy/ethical AI, and (iv) what are the past trends? the articles revealed an unbalanced distribution across the various dimensions, together with an exponential growth over recent years. Building upon our analysis, we argue for an increase in interdisciplinary research that shifts the focus from the currently dominant technological focus towards a more human-centered (educational and societal) focus. Only through such a development AI can contribute effectively to the UN Sustainable Development Goal no. 4 of a world with equitable and universal access to quality education. the results of our systematic review provide the basis to address and facilitate equality in the future AI&ED progress across regions worldwide.

关键词： Trustworthy and ethical AI AI&ED Web of science articles Systematic literature review Informatics and information technologies education and learning sciences Sustainable digital transformations

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Exploring ChatGPT's impact on post-secondary education: A qualitative study 23

Exploring ChatGPT's impact on post-secondary education: A qu...

引用

25th Western Canadian conference on Computing education (WCCCE)

作者： Rajabi, Parsa Taghipour, Parnian Cukierman, Diana Doleck, Tenzin Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9798400707896

As Chat Generative Pre-trained Transformer (ChatGPT) gains traction, its impact on post-secondary education is increasingly being debated. this qualitative study explores the perception of students and faculty members at a research university in Canada regarding ChatGPT's use in a post-secondary setting, focusing on how it could be incorporated and what ways instructors can respond to this technology. We present the summary of a discussion that took place in a two-hour focus group session with 40 participants from the computer science and engineering departments, and highlight issues surrounding plagiarism, assessment methods, and the appropriate use of ChatGPT. Findings suggest that students are likely to use ChatGPT, but there is a need for specific guidelines, more classroom assessments, and mandatory reporting of ChatGPT use. the study contributes to the emergent research on ChatGPT in higher education and emphasizes the importance of proactively addressing challenges and opportunities associated with ChatGPT adoption and use.

关键词： ChatGPT conversational AI Artificial Intelligence in education post-secondary higher education assessment education

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Research on Two-Stage Text Language Identification Algorithm for Chinese, Japanese, and Korean 25th

Research on Two-Stage Text Language Identification Algorith...

引用

25th International conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Qasim, Mamtimin Silamu, Wushour Qiu, Minghui School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou511363 China School of Information Science and Engineering Xinjiang University Urumqi830046 China Key Multi-lingual Laboratory of Xinjiang Urumqi830046 China

ISBN: (纸本)9789819642069

Existing open-source text language identification tools are not ideal for recognizing short texts in Chinese, Japanese, and Korean, and some of them cannot distinguish between Simplified and Traditional Chinese. To improve identification efficiency, this paper proposes a two-stage language identification algorithm for Chinese, Japanese, and Korean. the first stage is based on a character script identification algorithm, according to the Unicode encoding range of Chinese, Japanese, and Korean, and determines the language. If the identification result is Chinese, the classification algorithm developed using the bidirectional long short-term memory (BiLSTM)-Attention model is used in the second stage to determine whether the language is Simplified or Traditional Chinese. the experimental results show that the proposed method improves the identification efficiency of Chinese, Japanese, and Korean short texts compared to existing open-source language identification tools. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chinese Japanese Korean script identification simplified Chinese text language identification traditional Chinese Unicode

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Introducing Computing to Underserved Tribal Communities through the Bebras Challenge 2023

Introducing Computing to Underserved Tribal Communities Thro...

引用

28th Annual conference on innovation and technology in computer science education (ITiCSE)

作者： Shah, Vipul Agarwal, Smita Tata Consultancy Serv Pune India Tata Steel Fdn New Delhi India

ISBN: (纸本)9798400701399

Tata Consultancy Services (TCS) in partnership with ACM India has been organizing the Bebras Challenge in India since 2018. To date, over 500,000 students have participated in the challenge. Tata Steel Foundation (TSF) initiated "the thousand Schools Program" in 2015, in remote regions of Odisha and Jharkhand to improve children's learning levels and expose them to new knowledge areas and skills. these regions are tribally dense, forested, and have hilly terrain. they are highly underserved in terms of schools and teachers. the majority of the students are first-generation learners with no home support. A large number of them fall out of schooling and get engaged in child labour. TCS and TSF started an experiment to assess if computing can be introduced to students from such underserved communities through Bebras tasks. For a period of 6-8 weeks in September-October 2022, around 10,000 students solved a total of 15 Bebras tasks of varying difficulty levels.

关键词： computational thinking (CT) Bebras challenge computer science education

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Knowledge Distillation of LLMs for Automatic Scoring of science Assessments 25th

Knowledge Distillation of LLMs for Automatic Scoring of Scie...

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25th International conference on Artificial Intelligence in education (AIED)

作者： Latif, Ehsan Fang, Luyang Ma, Ping Zhai, Xiaoming AI4STEM Educ Ctr Athens GA 30602 USA Univ Georgia Dept Math Sci & Social Studies Educ Athens GA 30602 USA Univ Georgia Dept Stat Athens GA 30602 USA

ISBN: (纸本)9783031643118;9783031643125

this study proposes a method for knowledge distillation (KD) of fine-tuned Large Language Models (LLMs) into smaller, more efficient, and accurate neural networks. We specifically target the challenge of deploying these models on resource-constrained devices. Our methodology involves training the smaller student model (Neural Network) using the prediction probabilities (as soft labels) of the LLM, which serves as a teacher model. this is achieved through a specialized loss function tailored to learn from the LLM's output probabilities, ensuring that the student model closely mimics the teacher's performance. To validate the performance of the KD approach, we utilized a large dataset, 7T, containing 6,684 student-written responses to science questions and three mathematical reasoning datasets with student-written responses graded by human experts. We compared accuracy with state-of-the-art (SOTA) distilled models, TinyBERT, and artificial neural network (ANN) models. Results have shown that the KD approach has 3% and 2% higher scoring accuracy than ANN and TinyBERT, respectively, and comparable accuracy to the teacher model. Furthermore, the student model size is 0.03M, 4,000 times smaller in parameters and x10 faster in inferencing than the teacher model and TinyBERT, respectively. the significance of this research lies in its potential to make advanced AI technologies accessible in typical educational settings, particularly for automatic scoring.

关键词： large language model (LLM) BERT knowledge distillation automatic scoring education technology

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Using a Two-Stage Final Exam in an Intro CS Course: Student Perceptions and Grade Impacts 23

Using a Two-Stage Final Exam in an Intro CS Course: Student ...

引用

25th Western Canadian conference on Computing education (WCCCE)

作者： Himbeault, Lauren Latulipe, Celine Univ Manitoba Winnipeg MB Canada

ISBN: (纸本)9798400707896

We present results from testing a two-stage exam format in a small, first-year programming class (n=24), including survey responses from students (n=8) about their experience with the exam format. Students reported liking the format due to a decrease in stress, helping them to better understand course concepts, and helping to improve their grades.

关键词： computer science education assessment two-stage exams

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Generation and Evaluation of a Culturally-Relevant CS1 Textbook for Latines using Large Language Models 29

Generation and Evaluation of a Culturally-Relevant CS1 Textb...

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29th Annual conference on innovation and technology in computer science education (ITiCSE)

作者： Molina, Ismael Villegas Montalvo, Audria Zhong, Shera Jordan, Mollie Raj, Adalbert Gerald Soosai Univ Calif San Diego La Jolla CA 92093 USA North Carolina State Univ Raleigh NC USA

ISBN: (纸本)9798400706004

In the United States, culturally relevant computing (CRC) is one of the most popular pedagogical implementations for Latin American (Latine) students. Culturally-relevant learning resources are a valuable tool for implementing CRC. However, the traditional method of creation and maintenance of textbooks takes a significant amount of time and effort. Given the duration required for textbook production, the development of culturally-relevant learning resources may become lengthened, as it requires close attention both on the material and the incorporation of cultural referents. In order to accelerate the process, we used the advancement of large language models (LLMs) to our advantage. through prompt engineering, we created a series of prompts to produce a textbook for an introductory computer science course (CS1) that incorporates Latine culture. this textbook was evaluated on metrics regarding sensibility, correctness, readability, linguistic approachability, appropriateness of examples, and cultural relevance. Overall, the generated textbook was mainly sensible, correct, readable, and linguistically approachable. Code examples were not always appropriate due to the usage of libraries that are not typically used in a CS1 course. the cultural relevance was apparent, but it often included surface-level cultural referents. the main incorporation of culture was through geographical locations and people's names. this suggests that the use of LLMs to generate textbooks may serve as a valuable first step for writing culturally-relevant learning resources. though this study focuses on Latines, our results and prompts may be applicable for generating culturally-relevant CS1 textbooks for other cultures.

关键词： Latine Latinx Latina Latino large language models culturally relevant resources computer science textbook resource generation

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Unveiling Android Malware: A Deep Learning Perspective Using RNN and LSTM 25

Unveiling Android Malware: A Deep Learning Perspective Using...

引用

25th International Arab conference on Information technology, ACIT 2024

作者： Alqammaz, Abdullah Al-Mousa, Mohammad Rasmi Zaid, Ali Abu Asassfeh, Mahmoud Rajab, Mohammad Abdulrazzaq Dajah, Sherien Alali, Abdulla M. F. Khouj, Mohammed Zarqa University Cyber Security Department AzZarqa Jordan University of Anbar Computer Science Department Anbar Iraq The World Islamic Sciences & Education University Management Department Amman Jordan Zarqa University Computer Scince Department AzZarqa Jordan University of Business and Technology Electrical Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

the advent and increasing popularity of Android devices have brought about undesirable mobile malware and its attendant applications that constitutes a danger to society in terms of privacy and information leakage. Because the Android open-source OS is attractive to criminals, it seems impossible for old methods of virus detection to be effective as they used to be. the present study tackles this vital problem by offering an innovative enhancement that uses deep learning algorithms: recurrent neural networks and long short-term memory networks for android malware detection. New solid classification schema was created which focused on the meta-information of applications in order to differentiate entre benign and malicious apps. In our experiments, both RNN and LSTM models yielded improvements in performance metrics which suggests that they can be used to counter the current computer malware challenges. this study adds to the body of knowledge available on the detection of malware and lays the foundation for other studies designed to employ other couplings of deep learning algorithms in further boosting the security of Android applications. © 2024 IEEE.

关键词： Android malware

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TeachNow: Enabling Teachers to Provide Spontaneous, Realtime 1:1 Help in Massive Online Courses 29

TeachNow: Enabling Teachers to Provide Spontaneous, Realtime...

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29th Annual conference on innovation and technology in computer science education (ITiCSE)

作者： Malik, Ali Woodrow, Juliette Wang, Chao Piech, Chris Stanford Univ Stanford CA 94305 USA Queens Univ Kingston ON Canada

ISBN: (纸本)9798400706004

One-on-one help from a teacher is highly impactful for students, yet extremely challenging to support in massive online courses (MOOCs). In this work, we present TeachNow: a novel system that lets volunteer teachers from anywhere in the world instantly provide 1:1 help sessions to students in MOOCs, without any scheduling or coordination overhead. TeachNow works by quickly finding an online student to help and putting them in a collaborative working session with the teacher. the spontaneous, on-demand nature of TeachNow gives teachers the flexibility to help whenever their schedule allows. We share our experiences deploying TeachNow as an experimental feature in a six week online CS1 course with 9,000 students and 600 volunteer teachers. Even as an optional activity, TeachNow was used by teachers to provide over 12,300 minutes of 1:1 help to 375 unique students. through a carefully designed randomised control trial, we show that TeachNow sessions increased student course retention rate by almost 15%. Moreover, the flexibility of our system captured valuable volunteer time that would otherwise go to waste. Lastly, TeachNow was rated by teachers as one of the most enjoyable and impactful aspects of their involvement in the course. We believe TeachNow is an important step towards providing more human-centered support in massive online courses.

关键词： Massive Online Courses Learning at Scale 1:1 Help Tutoring Personalised Learning education CS1 Human-Centered Learning

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Inclusive GroupWork Assessment for Cybersecurity 2023

Inclusive GroupWork Assessment for Cybersecurity

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28th Annual conference on innovation and technology in computer science education (ITiCSE)

作者： Xiao, Hannan Spring, Joseph Kuzminykh, Ievgeniia Cortellazzi, Jacopo Kings Coll London Dept Informat London England Univ Hertfordshire Dept Comp Sci Hatfield England

ISBN: (纸本)9798400701399

this poster presents an ongoing study that takes a diverse and inclusive approach to designing a practical group work assessment for an undergraduate cybersecurity course delivered to third year university cohorts. Students were given the choice to either work individually or as part of a group to complete the assignment in virtual laboratories. the study evaluates how student grouping preferences change when the teaching structure adapts in response to the the Covid-19 pandemic and how grouping preference impacts upon academic performance. Students reflected positively on the assignment and demonstrated a preference for treating group information as private. No disputes regarding group marks or contributions from different group members arose despite the number of groups involved. Students have taken responsibility for their choices and have accepted the outcomes of their teamwork.

关键词： diversity and inclusion group work assessment cybersecurity education virtual laboratory

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