Dr Anthony 'Skip' Basiel – Blended Learning Solutions

SchoRes ICSSH 2025 21/04/2025

SchoRes International Conference on Social Sciences & Humanities

I have been asked to be keynote speaker at this conference. See https://www.schores.org/conferences_details.php?acid=124

Here is the abstract and conclusion for the paper we are submitting below. Please email abasiel@gmail.com if you would like the full draft paper. This is a sample AI video I made to demonstrate how we can use AI to make a learning simulation:

Abstract
This paper explores the theory and practice of using artificial intelligence (AI) in curriculum and assessment design. The objectives are to apply AI in four stages. First, using AI in a multiple-choice question student-generated pre-test. This helps establish prior or tacit knowledge of the learners and stakeholders. Second, using AI in conjunction with blended learning role-plays or simulations. The third stage uses AI to construct and evaluate a post-simulation debriefing. Finally, in assessment stage four, AI is used to map the learning objectives to the evidence in the video recording transcripts. A case study methodology gathers evidence through surveys, interviews, video recorded observations and AI analysis of transcript text. The study concludes that AI can be integrated into the various stages of curriculum and assessment design in the role of a ‘thought partner’ as well as content producer. In these early days of applying AI to teaching, learning pedagogy and assessment an evidence-based approach to this innovative convergence of technology is recommended.

Keywords: artificial intelligence, assessment, curriculum, debriefing, pre-test, role-play, simulation, transcript evaluation.

1. Conclusion

The objectives of this paper are to apply AI in four stages of the curriculum and assessment of learning simulation designs. The first stage used AI in a multiple-choice question student-generated pre-test. Learners answered questions and critically reviewed topics. AI was demonstrated in conjunction with blended learning role-plays or simulations by producing possible scenario descriptions and/or scripts. AI was used to construct and evaluate a post-simulation debriefing script in the next stage of the model learning objectives were linked to key words as evidence to support its use in assessment.

Limitations

The various pilot case studies presented in this paper were conducted with small sample size groups. Further case studies using the 4-stage AI learning simulations model are needed to gather more evidence from cross-disciplinary use.

Recommendations

Each stage of the application of AI to the curriculum and assessment models presented in this paper can be applied to the context of the reader through these recommendations:

• As the pre-test question pool grows, a review of the quality of the MCQs can filter out weaker samples.
• Simulations and role plays can be synthesized with traditional instructional materials to provide learners’ a safe setting to develop knowledge, skills and confidence.
• AI has the potential to support debriefing and reflection on learning through more applications of transcript analysis.

The authors encourage readers to contact them for collaboration opportunities to further research and develop the learning models and technologies introduced in this paper.