MargVidhi: An AI-Assisted Prototype for Legal Education and Case Analysis in Indian Motor Vehicle Law

People encountering a road accident describe what happened, not which section of the Motor Vehicles Act, 1988 might apply.MargVidhi is a browser-based prototype built around a short conversation to help with that lookup.The local corpus for this build holds 60 curated entries.Google Gemini can be used to talk through the user's scenario, but the prototype does not depend on it: a deterministic fallback covers a small set of common topics.In practice the controller asks a few clarifying questions, drafts a case summary for the user's approval, and only then starts retrieval.Fuse.js searches the local corpus across keywords, titles, and explanations, and the matches are shown as cards with the statutory wording kept separate from the plain-language explanation, penalty, suggested action, and forum.Users type their description at present; a reusable speech-recognition component and language settings for English, Hindi, Tamil, Telugu, and Bengali sit in the codebase but voice input is not wired to the current analysis screen.This paper describes how we built the prototype, what we checked functionally, and where it falls short.With no labelled benchmark or user study, we make no claim about legal accuracy.

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Publication Details

Journal
IARJSET
Published
2026-10-29
DOI
https://doi.org/10.17148/iarjset.2026.131060
Primary Topic
Artificial Intelligence in Law
Type
article
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article

MargVidhi: An AI-Assisted Prototype for Legal Education and Case Analysis in Indian Motor Vehicle Law

Tarun S, Darshan H, Rakshan Appachu KB, Arnav Adithya S
IARJSET
Artificial Intelligence in Law
article

MargVidhi: An AI-Assisted Prototype for Legal Education and Case Analysis in Indian Motor Vehicle Law

Tarun S, Darshan H, Rakshan Appachu KB, Arnav Adithya S
article en

Abstract

People encountering a road accident describe what happened, not which section of the Motor Vehicles Act, 1988 might apply.MargVidhi is a browser-based prototype built around a short conversation to help with that lookup.The local corpus for this build holds 60 curated entries.Google Gemini can be used to talk through the user's scenario, but the prototype does not depend on it: a deterministic fallback covers a small set of common topics.In practice the controller asks a few clarifying questions, drafts a case summary for the user's approval, and only then starts retrieval.Fuse.js searches the local corpus across keywords, titles, and explanations, and the matches are shown as cards with the statutory wording kept separate from the plain-language explanation, penalty, suggested action, and forum.Users type their description at present; a reusable speech-recognition component and language settings for English, Hindi, Tamil, Telugu, and Bengali sit in the codebase but voice input is not wired to the current analysis screen.This paper describes how we built the prototype, what we checked functionally, and where it falls short.With no labelled benchmark or user study, we make no claim about legal accuracy.

IARJSETVol. 13(10)
Assam Don Bosco University (IN), Universidad Don Bosco (SV), Don Bosco Institute of Technology (IN)
Peace, justice, and strong institutions
Openalex Percentile: Top 4%
Artificial Intelligence in Law
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