Copertina

Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Prather, James, Leinonen, Juho, Kiesler, Natalie, Benario, Jamie Gorson, Lau, Sam, MacNeil, Stephen, Norouzi, Narges, Opel, Simone, Pettit, Vee, Porter, Leo, Reeves, Brent N., Savelka, Jaromir, Smith IV, David H., Strickroth, Sven, Zingaro, Daniel
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computers and Society Artificial Intelligence Human-Computer Interaction Software Engineering
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

CS1-LLM: Integrating LLMs into CS1 Instruction
di: Vadaparty, Annapurna, et al.
Pubblicazione: (2024)

Integrating Large Language Models and Evaluating Student Outcomes in an Introductory Computer Science Course
di: Vadaparty, Annapurna, et al.
Pubblicazione: (2025)

The Robots are Here: Navigating the Generative AI Revolution in Computing Education
di: Prather, James, et al.
Pubblicazione: (2023)

Feedback-Generation for Programming Exercises With GPT-4
di: Azaiz, Imen, et al.
Pubblicazione: (2024)

Desirable Characteristics for AI Teaching Assistants in Programming Education
di: Denny, Paul, et al.
Pubblicazione: (2024)

The Role of Generative AI in Software Student CollaborAItion
di: Kiesler, Natalie, et al.
Pubblicazione: (2025)

Open, Small, Rigmarole -- Evaluating Llama 3.2 3B's Feedback for Programming Exercises
di: Azaiz, Imen, et al.
Pubblicazione: (2025)

Prompts First, Finally
di: Reeves, Brent N., et al.
Pubblicazione: (2024)

Probing the Unknown: Exploring Student Interactions with Probeable Problems at Scale in Introductory Programming
di: Denny, Paul, et al.
Pubblicazione: (2025)

The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
di: Prather, James, et al.
Pubblicazione: (2024)

Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models
di: Prather, James, et al.
Pubblicazione: (2024)

Integrating Natural Language Prompting Tasks in Introductory Programming Courses
di: Kerslake, Chris, et al.
Pubblicazione: (2024)

Fast and Forgettable: A Controlled Study of Novices' Performance, Learning, Workload, and Emotion in AI-Assisted and Human Pair Programming Paradigms
di: Gardella, Nicholas, et al.
Pubblicazione: (2026)

Using Large Language Models to Enhance Programming Error Messages
di: Leinonen, Juho, et al.
Pubblicazione: (2022)

Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice
di: Westermann, Hannes, et al.
Pubblicazione: (2024)

MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling
di: Krishnan, Dhriti, et al.
Pubblicazione: (2026)

Assessing Problem Decomposition in CS1 for the GenAI Era
di: Srinath, Samvrit, et al.
Pubblicazione: (2025)

Drawing Your Programs: Exploring the Applications of Visual-Prompting with GenAI for Teaching and Assessment
di: Smith IV, David H., et al.
Pubblicazione: (2026)

Personalized Worked Example Generation from Student Code Submissions Using Pattern-based Knowledge Components
di: Pitts, Griffin, et al.
Pubblicazione: (2026)

Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skills
di: Denny, Paul, et al.
Pubblicazione: (2024)

Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free
di: Zhang, Li, et al.
Pubblicazione: (2026)

It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Human
di: Harasta, Jakub, et al.
Pubblicazione: (2024)

Do LLMs Truly Understand When a Precedent Is Overruled?
di: Zhang, Li, et al.
Pubblicazione: (2025)

Experiences from Integrating Large Language Model Chatbots into the Classroom
di: Hellas, Arto, et al.
Pubblicazione: (2024)

Evaluating Contextually Personalized Programming Exercises Created with Generative AI
di: Logacheva, Evanfiya, et al.
Pubblicazione: (2024)

Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners
di: Prather, James, et al.
Pubblicazione: (2024)

Patterns of Student Help-Seeking When Using a Large Language Model-Powered Programming Assistant
di: Sheese, Brad, et al.
Pubblicazione: (2023)

Using LLMs to Discover Legal Factors
di: Gray, Morgan, et al.
Pubblicazione: (2024)

The Data Dilemma: Authors' Intentions and Recognition of Research Data in Educational Technology Research
di: Schulz, Sandra, et al.
Pubblicazione: (2025)

Students' Feedback Requests and Interactions with the SCRIPT Chatbot: Do They Get What They Ask For?
di: Scholl, Andreas, et al.
Pubblicazione: (2025)

GenAI Voice Mode in Programming Education
di: Jacobs, Sven, et al.
Pubblicazione: (2025)

How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course
di: Scholl, Andreas, et al.
Pubblicazione: (2024)

Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education
di: Jain, Mragisha, et al.
Pubblicazione: (2026)

The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance
di: Mihaylova, Tsvetomila, et al.
Pubblicazione: (2026)

The Missing Evaluation Axis: What 10,000 Student Submissions Reveal About AI Tutor Effectiveness
di: Niousha, Rose, et al.
Pubblicazione: (2026)

"Sometimes You Just Gotta Risk It for the Biscuit": A Portrait of Student Risk-Taking
di: Leinonen, Juho, et al.
Pubblicazione: (2024)

Synthetic Students: A Comparative Study of Bug Distribution Between Large Language Models and Computing Students
di: MacNeil, Stephen, et al.
Pubblicazione: (2024)

LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education
di: Leinonen, Juho, et al.
Pubblicazione: (2024)

Measuring Faithfulness and Abstention: An Automated Pipeline for Evaluating LLM-Generated 3-ply Case-Based Legal Arguments
di: Zhang, Li, et al.
Pubblicazione: (2025)

Changes in Coding Behavior and Performance Since the Introduction of LLMs
di: Zhang, Yufan, et al.
Pubblicazione: (2026)