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AI in Education

Sep 25, 2024, 4 min read

  • 1 Fine tunes a transformer model using a more diverse short-form QA dataset for the purposes of AutoGrading. Notably ,it achieves more trust via more accuracy by allowing humans to manually grade difficult questions determined by a certain threshold of desired accuracy . It achieves better performance than other models, at the cost of higher variability .

  • 2 An analysis into the current state of resources for learning about AI. It finds that most existing resources are teacher-unfriendly (i.e., hard to use for teachers.)

  • 3 Introduces C3-IoC, a career guidance system for IT which enables users to identify their hard and soft skills as well as where they land in the job landscape.

  • 4 Conducts a survey on how Artificial Intelligence has been used to make a smarter and more adaptive education system. AI allows education systems to be more adaptive and interactive.

  • 5 Studies how to retrieve documents to provide elaborative feedback to students, and how to post-process this feedback.

  • 6 PREREQ Paper. It aims to infer pre-requisite relations between particular concepts based on data gathered from MOOCs.

  • 7 Cognitive systems like IBM Watson can only replace tutors (used to answer common questions) in a limited fashion, especially when they are deterministic, and unable to infer intent.

  • 8 A position paper that argues the problem of Automated Scoring requires a multi-disciplinary view that focuses not only on the application and advancement of NLP techniques, but also a consideration on how stakeholders will deploy, use, and be affected by Automated Scoring technology.

  • 9 Introduces Rubric Sampling. It generatively infers how likely a student response is given some misconceptions about the topic.

  • 10 Introduces Beetle II - a rule-based system that facilitates learning for a complex topic (electronics) by allowing students to experiment and by giving elaborative feedback via a symbolic NLP system coupled with domain knowledge.

  • 11 Introduces Lurch, a word processor that can assess whether statements in a document are mathematically sound. The paper focuses on the UX and the evaluation of the UX rather than the system itself. It is more geared towards education than being a powerful proof engine.

  • 12 A survey of Self Regulated Learning in pedagogical agents.

  • 13 A proposal for use of a multi-agent pedagogical systems to teach students computer science. The agent consists of personalized agents to teach a subject, assess students, and improve the curriculum.

Footnotes

  1. Schneider, J., Richner, R., & Riser, M. (2023). Towards Trustworthy AutoGrading of Short, Multi-lingual, Multi-type Answers ↩

  2. Druga, S., Otero, N., & Ko, A. J. (2022). The Landscape of Teaching Resources for AI Education ↩

  3. José-García, A., et al. C3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation ↩

  4. Bezerra, R. M. A., DurãEs, D., & Novais, P. N. (2022). Survey for Smart and Adaptative Education ↩

  5. Olney, A. M. (2021). Generating Response-Specific Elaborated Feedback Using Long-Form Neural Question Answering ↩

  6. Roy, S., et al. (2019). Inferring Concept Prerequisite Relations from Online Educational Resources ↩

  7. Müller, S., Bergande, B., & Brune, P. (2018). Robot Tutoring: On the Feasibility of Using Cognitive Systems as Tutors in Introductory Programming Education: A Teaching Experiment. ↩

  8. Madnani, N., & Cahill, A. (2018). Automated Scoring: Beyond Natural Language Processing ↩

  9. Wu, M., et al. (2018). Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference ↩

  10. Dzikovska, M., et al. BEETLE II: Deep Natural Language Understanding and Automatic Feedback Generation for Intelligent Tutoring in Basic Electricity and Electronics ↩

  11. Carter, N., & Monks, K. (2013, July). Lurch: A word processor that can grade students’ proofs ↩

  12. Graesser, A., & McNamara, D. (2010). Self-Regulated Learning in Learning Environments With Pedagogical Agents That Interact in Natural Language ↩

  13. Shi, H., Shang, Y., & Chen, S.-S. (2000). A Multi-Agent System for Computer Science Education ↩