AI-Driven Innovations in Education and Assessment

Advances in AI-Driven Education and Assessment

Recent developments in the field of AI-driven education and assessment are pushing the boundaries of how technology can support and enhance learning experiences. The focus is increasingly on creating personalized, scalable, and robust solutions that cater to diverse educational needs. Innovations in large language models (LLMs) are at the forefront, with significant advancements in their application for course generation, tutoring, and automated grading. These models are being fine-tuned to better align with pedagogical principles, ensuring that they not only assist in content creation but also enhance learning outcomes.

One of the major trends is the integration of LLMs into educational frameworks to support non-native English speakers and bilingual learners, addressing language barriers that have historically hindered participation in STEM fields. Additionally, there is a growing emphasis on the ethical use of AI in education, with frameworks being developed to ensure fairness, reliability, and transparency in AI-driven assessments.

The field is also witnessing a shift towards more practical and accessible tools for educators, such as handheld document scanning and transcription technologies, which leverage LLMs to streamline administrative tasks and provide more efficient ways to digitize and analyze educational materials.

Noteworthy papers include:

  • Generative AI and Agency in Education: A critical review highlighting the dual-edged impact of generative AI on learner autonomy and educational equity.
  • Artificial Intelligence Driven Course Generation: Demonstrates the transformative potential of AI in creating high-quality, personalized course materials efficiently.
  • Leveraging LLM Tutoring Systems for Non-Native English Speakers: Offers valuable insights into how LLMs can bridge language gaps in STEM education.
  • Towards Scalable Automated Grading: Explores the feasibility of using LLMs for grading, showing promise in providing scalable and consistent assessment solutions.

Sources

A Novel Psychometrics-Based Approach to Developing Professional Competency Benchmark for Large Language Models

GLAT: The Generative AI Literacy Assessment Test

Handheld Video Document Scanning: A Robust On-Device Model for Multi-Page Document Scanning

Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis

From chalkboards to chatbots: SELAR assists teachers in embracing AI in the curriculum

Artificial Intelligence Driven Course Generation: A Case Study Using ChatGPT

Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education

Towards the design of model-based means and methods to characterize and diagnose teachers' digital maturity

An Exploration of Higher Education Course Evaluation by Large Language Models

Where Assessment Validation and Responsible AI Meet

Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Courses

ChatGPT in Research and Education: Exploring Benefits and Threats

Unlocking the Archives: Using Large Language Models to Transcribe Handwritten Historical Documents

Towards Scalable Automated Grading: Leveraging Large Language Models for Conceptual Question Evaluation in Engineering

Improving Bilingual Capabilities of Language Models to Support Diverse Linguistic Practices in Education

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