An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers

• URL: http://arxiv.org/abs/2112.01229v1
• Date: Tue, 9 Nov 2021 06:15:13 GMT
• Title: An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers
• Authors: Yong-Bin Kang, Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman, Natalie Wieland, Elizabeth Kolliasl, Hung Du, Steven Thomson, Yuan-Fang Li
• Abstract summary: One of the most difficult part of scaling digital learning and teaching is to be able to assess learner's knowledge and competency. We propose an Artificial Intelligence-based solution namely VidVersityQG for generating questions automatically from pre-recorded video lectures.
• Score: 6.0988393123743485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There has been a recent and rapid shift to digital learning hastened by the pandemic but also influenced by ubiquitous availability of digital tools and platforms now, making digital learning ever more accessible. An integral and one of the most difficult part of scaling digital learning and teaching is to be able to assess learner's knowledge and competency. An educator can record a lecture or create digital content that can be delivered to thousands of learners but assessing learners is extremely time consuming. In the paper, we propose an Artificial Intelligence (AI)-based solution namely VidVersityQG for generating questions automatically from pre-recorded video lectures. The solution can automatically generate different types of assessment questions (including short answer, multiple choice, true/false and fill in the blank questions) based on contextual and semantic information inferred from the videos. The proposed solution takes a human-centred approach, wherein teachers are provided the ability to modify/edit any AI generated questions. This approach encourages trust and engagement of teachers in the use and implementation of AI in education. The AI-based solution was evaluated for its accuracy in generating questions by 7 experienced teaching professionals and 117 education videos from multiple domains provided to us by our industry partner VidVersity. VidVersityQG solution showed promising results in generating high-quality questions automatically from video thereby significantly reducing the time and effort for educators in manual question generation.

Related papers

• AI-Powered Math Tutoring: Platform for Personalized and Adaptive Education [0.0]
  We introduce a novel multi-agent AI tutoring platform that combines adaptive and personalized feedback, structured course generation, and textbook knowledge retrieval.<n>This system allows students to learn new topics while identifying and targeting their weaknesses, revise for exams effectively, and practice on an unlimited number of personalized exercises.
  arXiv  Detail & Related papers  (2025-07-14T20:35:16Z)
• Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos [6.689443785478135]
  We investigate the capabilities of vision-language models for generating learning-oriented questions for educational videos.<n>Our findings delineate the capabilities of current vision-language models, highlighting the need for fine-tuning and addressing challenges in question diversity and relevance.
  arXiv  Detail & Related papers  (2025-05-03T11:37:31Z)
• INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models [0.7499722271664147]
  INSIGHT is a proof of concept to combine various AI tools to assist teaching staff and students in the process of solving exercises. We analyze students' questions to an LLM by extracting keywords, which we use to dynamically build an FAQ from students' questions.
  arXiv  Detail & Related papers  (2025-04-24T15:47:20Z)
• GRADEO: Towards Human-Like Evaluation for Text-to-Video Generation via Multi-Step Reasoning [62.775721264492994]
  GRADEO is one of the first specifically designed video evaluation models. It grades AI-generated videos for explainable scores and assessments through multi-step reasoning. Experiments show that our method aligns better with human evaluations than existing methods.
  arXiv  Detail & Related papers  (2025-03-04T07:04:55Z)
• Investigating Developers' Preferences for Learning and Issue Resolution Resources in the ChatGPT Era [1.3124513975412255]
  The landscape of software developer learning resources has continuously evolved, with recent trends favoring engaging formats like video tutorials. The emergence of Large Language Models (LLMs) like ChatGPT presents a new learning paradigm. We conducted a survey targeting software developers and computer science students, gathering 341 responses, of which 268 were completed and analyzed.
  arXiv  Detail & Related papers  (2024-10-10T22:57:29Z)
• Video as the New Language for Real-World Decision Making [100.68643056416394]
  Video data captures important information about the physical world that is difficult to express in language. Video can serve as a unified interface that can absorb internet knowledge and represent diverse tasks. We identify major impact opportunities in domains such as robotics, self-driving, and science.
  arXiv  Detail & Related papers  (2024-02-27T02:05:29Z)
• How to Build an AI Tutor that Can Adapt to Any Course and Provide Accurate Answers Using Large Language Model and Retrieval-Augmented Generation [0.0]
  The OpenAI Assistants API allows AI Tutor to easily embed, store, retrieve, and manage files and chat history. The AI Tutor prototype demonstrates its ability to generate relevant, accurate answers with source citations.
  arXiv  Detail & Related papers  (2023-11-29T15:02:46Z)
• Learning to Reuse Distractors to support Multiple Choice Question Generation in Education [19.408786425460498]
  This paper studies how a large existing set of manually created answers and distractors can be leveraged to help teachers in creating new multiple choice questions (MCQs) We built several data-driven models based on context-aware question and distractor representations, and compared them with static feature-based models. Both automatic and human evaluations indicate that context-aware models consistently outperform a static feature-based approach.
  arXiv  Detail & Related papers  (2022-10-25T12:48:56Z)
• Multimodal Lecture Presentations Dataset: Understanding Multimodality in Educational Slides [57.86931911522967]
  We test the capabilities of machine learning models in multimodal understanding of educational content. Our dataset contains aligned slides and spoken language, for 180+ hours of video and 9000+ slides, with 10 lecturers from various subjects. We introduce PolyViLT, a multimodal transformer trained with a multi-instance learning loss that is more effective than current approaches.
  arXiv  Detail & Related papers  (2022-08-17T05:30:18Z)
• Self-Supervised Learning for Videos: A Survey [70.37277191524755]
  Self-supervised learning has shown promise in both image and video domains. In this survey, we provide a review of existing approaches on self-supervised learning focusing on the video domain.
  arXiv  Detail & Related papers  (2022-06-18T00:26:52Z)
• CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures [6.25256391074865]
  We propose a new unified model, CLUE, which learns from the features extracted from public online teaching videos. Our model exploits various multi-modal features to model the complexity of language, context information, textual emotion of the delivered content.
  arXiv  Detail & Related papers  (2022-01-14T19:51:06Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)
• Artificial Intelligence enabled Smart Learning [0.0]
  Artificial Intelligence (AI) is a discipline of computer science that deals with machine intelligence. It helps in analysing the enormous amounts of data that is collected from individual students, teachers and academic staff. The World Bank report on education has indicated that the learning gap created by this problem causes many students to drop out.
  arXiv  Detail & Related papers  (2021-01-08T12:49:33Z)
• Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms [50.19997675066203]
  We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
  arXiv  Detail & Related papers  (2020-05-16T02:17:04Z)
• Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience [76.9910678786031]
  We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting. Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
  arXiv  Detail & Related papers  (2020-01-24T22:52:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.