Towards an AI Course Based on Neural Networks

Source publication / research team or educational organization described in paper

USA

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

• Research / curriculum design context

Course implementation or course design

4 hours; 9 hours

Not specified in extracted text

ML concepts / supervised learning, 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 a prior paper, we argued that Artificial Intelligence (AI) should be placed on a different foundation, one based on pat- tern recognition and feature learning rather than symbol ma- nipulation and feature engineering.

• 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.

ML concepts / supervised learning, Assessment / tutoring analytics

Not specified in extracted text

• Tutor
• Evaluator

• Primary interaction pattern inferred from publication: Curriculum / course design, Assessment support.
• AI capability focus: ML concepts / supervised learning, 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.
• In this paper, we provide a proof of concept of an AI course that follows that proposed approach.

• In this paper, we provide a proof of concept of an AI course that follows that proposed approach.
• Students study how these systems become so in- credibly powerful through machine learning of features and through pattern matching.
• Students learn how those systems represent knowledge and they study their currently limited reasoning abilities.

In a prior paper, we argued that Artificial Intelligence (AI) should be placed on a different foundation, one based on pat- tern recognition and feature learning rather than symbol ma- nipulation and feature engineering. In this paper, we provide a proof of concept of an AI course that follows that proposed approach.

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

• Activity documentation

• 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
• Curriculum / course design
• Assessment support
• ML concepts / supervised learning
• Assessment / tutoring analytics

• Completed

Unspecified / broad education

Research / curriculum design context

ML concepts / supervised learning, Assessment / tutoring analytics

Tutoring / feedback-supported learning

Low to Medium

Medium

Towards an AI Course Based on Neural Networks

• Michael Wollowski

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35179

https://ojs.aaai.org/index.php/AAAI/article/view/35179

https://ojs.aaai.org/index.php/AAAI/article/view/35179/37334

016_Towards an AI Course Based on Neural Networks.pdf

9

In a prior paper, we argued that Artificial Intelligence (AI) should be placed on a different foundation, one based on pat- tern recognition and feature learning rather than symbol ma- nipulation and feature engineering. In this paper, we provide a proof of concept of an AI course that follows that proposed approach. Students study how these systems become so in- credibly powerful through machine learning of features and through pattern matching. Students learn how those systems represent knowledge and they study their currently limited reasoning abilities. Students spend time discussing the ac- complishments of current systems, positive as well as nega- tive and they study the projected impact of anticipated sys- tems. In this paper, we give a brief argument of why one would want to offer such a course. We present a detailed outline of the contents of such a course, together with learn- ing materials and their proposed use. We summarize relevant anonymous student feedback and offer a subjective evaluation of the pilot course. Course Materials — https://www.rose-hulman.edu/class/cs/csse313/schedule/

• 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

• group_size

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.