Learning Logical Reasoning Using an Intelligent Tutoring System: A Hybrid Approach to Student Modeling

Source publication / research team or educational organization described in paper

Canada

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

(a bayesian learner model). We conducted a study and collected data from nearly 300 students who processed 48 reasoning activities. These data were used to develop a psychometric model for initializing the learne; en his response vector. Data Collection Participants and procedures. A total of 294 participants were recruited online via the Prolific Academic platform. Materials. For each of the 16 items classes, three items were

Assessment / tutoring analytics

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

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.
• In our previous works, we presented Logic-Muse as an Intelli- gent Tutoring System that helps learners improve logical rea- soning skills in multiple contexts.

• 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.

Assessment / tutoring analytics

Not specified in extracted text

• Tutor

• Primary interaction pattern inferred from publication: Instructional design / AI education.
• AI capability focus: Assessment / tutoring analytics.

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• 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.

• Tutoring / feedback-supported learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

• Case classified under: Published empirical study.
• Pedagogical pattern: Tutoring / feedback-supported 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.
• A Bayesian net- work with expert validation has been developed and used in a Bayesian Knowledge Tracing (BKT) process that allows the inference of the learner skills.

• A Bayesian net- work with expert validation has been developed and used in a Bayesian Knowledge Tracing (BKT) process that allows the inference of the learner skills.
• This paper presents an eval- uation of the learner-model components in Logic-Muse (a bayesian learner model).

In our previous works, we presented Logic-Muse as an Intelli- gent Tutoring System that helps learners improve logical rea- soning skills in multiple contexts. Logic-Muse components were validated and argued by experts throughout the design- ing process (ITS researchers, logicians, and reasoning psy- chologists).

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• 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
• Instructional design / AI education
• Assessment / tutoring analytics

• Completed

Unspecified / broad education

Research / curriculum design context

Assessment / tutoring analytics

Tutoring / feedback-supported learning

Low to Medium

Medium

Learning Logical Reasoning Using an Intelligent Tutoring System: A Hybrid Approach to Student Modeling

• Roger Nkambou
• Janie Brisson
• Ange Tato
• Serge Robert

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

2023

10.1609/aaai.v37i13.26891

https://ojs.aaai.org/index.php/AAAI/article/view/26891

https://ojs.aaai.org/index.php/AAAI/article/view/26891/26663

082_Learning Logical Reasoning Using an Intelligent Tutoring System_ A Hybrid Approach to Student Modeling.pdf

8

In our previous works, we presented Logic-Muse as an Intelli- gent Tutoring System that helps learners improve logical rea- soning skills in multiple contexts. Logic-Muse components were validated and argued by experts throughout the design- ing process (ITS researchers, logicians, and reasoning psy- chologists). A catalog of reasoning errors (syntactic and se- mantic) has been established, in addition to an explicit repre- sentation of semantic knowledge and the structures and meta- structures underlying conditional reasoning. A Bayesian net- work with expert validation has been developed and used in a Bayesian Knowledge Tracing (BKT) process that allows the inference of the learner skills. This paper presents an eval- uation of the learner-model components in Logic-Muse (a bayesian learner model). We conducted a study and collected data from nearly 300 students who processed 48 reasoning activities. These data were used to develop a psychometric model for initializing the learner’s model and validating the structure of the initial Bayesian network. We have also devel- oped a neural architecture on which a model was trained to support a deep knowledge tracing (DKT) process. The pro- posed neural architecture improves the initial version of DKT by allowing the integration of expert knowledge (through the Bayesian Expert Validation Network) and allowing bet- ter generalization of knowledge with few samples. The results show a significant improvement in the predictive power of the learner model. The analysis of the results of the psychometric model also illustrates an excellent potential for improving the Bayesian network’s structure and the learner model’s initial- ization process.

• Research / curriculum design context

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

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

• 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.