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2023 Fiscal Year Research-status Report

Dynamic Genre-Specific Automated Online Feedback Tool

Research Project

Project/Area Number 23K00656
Research InstitutionThe University of Aizu

Principal Investigator

CARR Nicholas  会津大学, コンピュータ理工学部, 准教授 (70965699)

Co-Investigator(Kenkyū-buntansha) Julian Villegas  会津大学, コンピュータ理工学部, 上級准教授 (50706281)
BLAKE John  会津大学, コンピュータ理工学部, 上級准教授 (80635954)
Project Period (FY) 2023-04-01 – 2026-03-31
KeywordsAutomative feedback / Dynamic assessment / Sociocultural theory / iCALL / L2 Writing
Outline of Annual Research Achievements

The Dynamic Genre-Specific Automated Online Feedback Tool we are developing is important because there is no online automated corrective feedback tool that provides dynamic feedback on the linguistic features of computer science writing. With the release of OpenAI, we are pursuing the incorporation of added features to our original tool - mainly targeting grammar errors. A thorough search of the literature shows that automated dynamic assessment of additional language writing is challenging due to not being able to predict learner errors. However, we are hoping to develop a tool which, through the use of generative AI, can take a step towards an interactionist type of automated dynamic assessment. The protoype is almost finished and multi-modal feedback explanations have been created.

Current Status of Research Progress
Current Status of Research Progress

3: Progress in research has been slightly delayed.

Reason

We are making good progress. However, due to the release of OpenAI and other generative AI after applying for this finding, we are investigating the expansion of the tool to not only target genre-specific errors of computer science but also grammatical errors. This addition has meant we are very slighty behind our original schedule of releasing the prototype version of the tool.

Strategy for Future Research Activity

We intend to spend some more time further exploring the possibility of using generative AI for our tool. Such investigation is important because it will allow our tool to provide greater assistance to users. Once this is complete, the prototype version of the tool will be available for students to use and we will conduct multiple rounds of usability tests to refine the tool this academic year. This will set up the primary project, which is investigate the learning effects of using the tool over a full semester in the following year. We are always planning on submitting articles/book chapters to publish the development phase of the tool later in 2024.

Causes of Carryover

Because of the release of OpenAI and other generative AI to the general public, we needed to spend more time considering how to develop the tool. While taking time to consider this, we did not use all of our planned budget as we considered how to incorporate generative AI - and as such did not spend all our allocated budget. Furthermore, conference presentations have been pushed back until the current academic year.

  • Research Products

    (1 results)

All 2024

All Presentation (1 results) (of which Int'l Joint Research: 1 results)

  • [Presentation] Dynamic Online Assessor: Scientific Genre-Specific Automated Feedback2024

    • Author(s)
      Nicholas Carr
    • Organizer
      Japanese Association for Language Teacher Computer Assisted Language Learning 2024
    • Int'l Joint Research

URL: 

Published: 2024-12-25  

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