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Found 26 papers, 5 papers with code
Large Language Models Meet User Interfaces: The Case of Provisioning Feedback
Incorporating Generative AI (GenAI) and Large Language Models (LLMs) in education can enhance teaching efficiency and enrich student learning.
"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings.
Generative AI for Education (GAIED): Advances, Opportunities, and Challenges
This survey article has grown out of the GAIED (pronounced "guide") workshop organized by the authors at the NeurIPS 2023 conference.
CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs
Timely, personalized feedback is essential for students learning programming.
Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models
In this paper, we propose a new way to teach programming with Prompt Problems.
Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
Unlike syntax errors, for which a compiler or interpreter can issue a message, logic errors can be subtle.
A Systematic Review of Aspect-based Sentiment Analysis (ABSA): Domains, Methods, and Trends
This review is one of the largest SLRs on ABSA, and also, to our knowledge, the first that systematically examines the trends and inter-relations among ABSA research and data distribution across domains and solution paradigms and approaches.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs
Responding to the thousands of student questions on online QA platforms each semester has a considerable human cost, particularly in computing courses with rapidly growing enrollments.
Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models
This study evaluates the performance of the GPT-3. 5 and GPT-4 models for classifying help requests from students in an introductory programming class.
The Robots are Here: Navigating the Generative AI Revolution in Computing Education
Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards LLMs and their use in computing education contexts.
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy
Learnersourcing offers great potential for scalable education through student content creation.
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts.
Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators
In parallel with this shift, a new essential skill is emerging -- the ability to construct good prompts for code-generating models.
Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources
In this study, we investigated the potential for LLMs to produce learning resources in an introductory programming context, by comparing the quality of the resources generated by an LLM with those created by students as part of a learnersourcing activity.
Learnersourcing in the Age of AI: Student, Educator and Machine Partnerships for Content Creation
Engaging students in creating novel content, also referred to as learnersourcing, is increasingly recognised as an effective approach to promoting higher-order learning, deeply engaging students with course material and developing large repositories of content suitable for personalized learning.
Computing Education in the Era of Generative AI
The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student learning.
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner.
Comparing Code Explanations Created by Students and Large Language Models
In this paper, we explore the potential of LLMs in generating explanations that can serve as examples to scaffold students' ability to understand and explain code.
"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers
Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy.
Many bioinformatics programming tasks can be automated with ChatGPT
However, despite a variety of educational efforts, learning to write code can be a challenging endeavor for both researchers and students in life science disciplines.
Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation
The introductory programming sequence has been the focus of much research in computing education.
Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language
GitHub Copilot is an artificial intelligence model for automatically generating source code from natural language problem descriptions.
Using Large Language Models to Enhance Programming Error Messages
Large language models can be used to create useful and novice-friendly enhancements to programming error messages that sometimes surpass the original programming error messages in interpretability and actionability.
Automatic Generation of Programming Exercises and Code Explanations using Large Language Models
Our analysis suggests that there is significant value in massive generative machine learning models as a tool for instructors, although there remains a need for some oversight to ensure the quality of the generated content before it is delivered to students.
DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions
We propose DeepQR, a novel neural-network model for AQQR that is trained using multiple-choice-question (MCQ) datasets collected from PeerWise, a widely-used learnersourcing platform.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
