H-AES: Towards Automated Essay Scoring for Hindi

Source publication / research team or educational organization described in paper

India

Researchers, educators, instructors, or facilitators as described in the source publication

• Research / curriculum design context

Classroom, course, or resource-based AI education activity

Not specified in extracted text

Not specified in extracted text

NLP / text classification, ML concepts / supervised learning

• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

Unspecified / broad education

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• The use of Natural Language Processing (NLP) for Auto- mated Essay Scoring (AES) has been well explored in the English language, with benchmark models exhibiting perfor- mance comparable to human scorers.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

NLP / text classification, ML concepts / supervised learning

Language context discussed in source publication

• Evaluator

• Primary interaction pattern inferred from publication: Assessment support.
• AI capability focus: NLP / text classification, ML concepts / supervised learning.

• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Instructional / curriculum-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

• Case classified under: Published empirical study.
• Pedagogical pattern: Instructional / curriculum-based learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• We employ classical feature-based Machine Learning (ML) and advanced end-to-end models, including LSTM Networks and Fine-Tuned Transformer Ar- chitecture, in our approach and derive results comparable to those in the English language domain.

• We employ classical feature-based Machine Learning (ML) and advanced end-to-end models, including LSTM Networks and Fine-Tuned Transformer Ar- chitecture, in our approach and derive results comparable to those in the English language domain.

The use of Natural Language Processing (NLP) for Auto- mated Essay Scoring (AES) has been well explored in the English language, with benchmark models exhibiting perfor- mance comparable to human scorers. However, AES in Hindi and other low-resource languages remains unexplored.

• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Design / conceptual evidence

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Assessment / feedback quality
• Assessment support
• NLP / text classification
• ML concepts / supervised learning

• Completed

Unspecified / broad education

Research / curriculum design context

NLP / text classification, ML concepts / supervised learning

Instructional / curriculum-based learning

High

High

H-AES: Towards Automated Essay Scoring for Hindi

• Shubhankar Singh
• Anirudh Pupneja
• Shivaansh Mital
• Cheril Shah
• Manish Bawkar
• Lakshman Prasad Gupta
• Ajit Kumar
• Yaman Kumar
• Rushali Gupta
• Rajiv Ratn Shah

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26894

https://ojs.aaai.org/index.php/AAAI/article/view/26894

https://ojs.aaai.org/index.php/AAAI/article/view/26894/26666

085_H-AES_ Towards Automated Essay Scoring for Hindi.pdf

9

The use of Natural Language Processing (NLP) for Auto- mated Essay Scoring (AES) has been well explored in the English language, with benchmark models exhibiting perfor- mance comparable to human scorers. However, AES in Hindi and other low-resource languages remains unexplored. In this study, we reproduce and compare state-of-the-art methods for AES in the Hindi domain. We employ classical feature-based Machine Learning (ML) and advanced end-to-end models, including LSTM Networks and Fine-Tuned Transformer Ar- chitecture, in our approach and derive results comparable to those in the English language domain. Hindi being a low- resource language, lacks a dedicated essay-scoring corpus. We train and evaluate our models using translated English es- says and empirically measure their performance on our own small-scale, real-world Hindi corpus. We follow this up with an in-depth analysis discussing prompt-specific behavior of different language models implemented.

• Research / curriculum design context

• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• group_size
• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.