Advanced Seminar on ArtificialIntelligence andInformatics Ⅱ

Advanced Seminar on ArtificialIntelligence andInformatics Ⅱ

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Graduate School Specialized Course (Seminar)

Graduate School of Engineering

1Year

HETDA7MCA1

2.00Credits

講義 (Lecture)

Spring semester 2026

(Spring semester)

Syoji Kobashi,Kouki Nagamune,Manabu Nii,Takayuki Wada

Graduate School of Engineering

Japanese

Japanese (Classes may be conducted in English depending on students’ preferences.)

9

Room 6313, Building 6, Himeji Engineering Campus
Every Thursday, 12:10–13:00
Students are encouraged to make an appointment in advance via the Q&A function of the Universal Passport (UNIPA).

Please contact the instructor via the Q&A function of the Universal Passport (UNIPA).

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4◎／3〇

N/a

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[Course Objectives]
This course aims to develop doctoral students’ ability to systematically organize their own research in terms of background, objectives, methodology, results, and significance, and to describe and explain it clearly and concisely. In addition, students will acquire the ability to logically articulate the novelty and originality of their research, to clarify research outcomes based on evidence such as experimental data and analytical results, and to fairly and objectively evaluate the validity of their research.

[Learning Outcomes]
Upon successful completion of this course, students will be able to:

1. Organize their research in terms of background, objectives, methodology, results, and significance, and describe and explain it in a clear and comprehensive manner.
2. Clearly demonstrate the novelty and originality of their research and explain it logically in comparison with prior studies.
3. Present research outcomes based on experimental data and numerical results, and explain their validity.
4. Objectively evaluate research outcomes using appropriate methods, including statistical tests and evaluation metrics.
5. Provide constructive discussions and comments on others’ research presentations based on academic evidence.

Subtitle
A Practical Seminar on Research Design, Presentation, and Evaluation

Keywords
Research design, novelty, originality, unmet needs, market research, feasibility, experimental measurement, statistical testing, research evaluation, research presentation, discussion

[Course Description]
This course is conducted in an omnibus format and aims to develop doctoral students’ ability to systematically organize their own research and logically demonstrate its novelty and originality. In addition, students will deepen their understanding of experimental measurement methods, data handling, and evaluation techniques including statistical testing, and acquire the ability to fairly and objectively evaluate research outcomes based on evidence. Through research presentations and discussions, the course seeks to enhance the advanced explanatory and discussion skills required of researchers.

[Course Schedule]
Session 1: Guidance
Explanation of the course objectives, structure, and evaluation methods, and confirmation of the role of research presentations and discussions.

Session 2: Identifying Unmet Needs and Defining Research Topics
Organizing research issues and formulating research topics based on social and academic backgrounds.

Session 3: Market Research and Literature Review
Reviewing market trends and prior studies in related fields to clarify the positioning of the research.

Session 4: Novelty, Originality, and Feasibility of Research
Organizing the novelty and originality of the research topic and examining its feasibility.

Session 5: Structuring Research Content and Methods of Description and Explanation
Learning how to logically structure research content and describe and explain it clearly and comprehensively.

Session 6: Presentation of Research Concepts and Peer Evaluation
Presenting research concepts and deepening understanding through peer evaluation and discussion.

Session 7: Design of Experimental Measurement Methods and Key Considerations
Learning key considerations and reproducibility issues in the design of experimental measurements.

Session 8: Data Acquisition and Preprocessing
Understanding methods for acquiring experimental data and approaches to data preprocessing.

Session 9: Fundamentals of Statistical Testing
Learning the fundamentals of statistical testing required for research evaluation.

Session 10: Selection of Evaluation Metrics and Interpretation of Results
Examining appropriate methods for selecting evaluation metrics and interpreting results.

Session 11: Practical Exercises in Research Evaluation Based on Experimental Results
Evaluating research outcomes using experimental data and conducting critical analysis.

Session 12: Research Trends in Computational Intelligence
Reviewing advanced studies related to computational intelligence technologies and understanding current research trends.

Session 13: Evaluation of Novelty and Originality in Research
Learning perspectives and criteria for evaluating the novelty and originality of research outcomes.

Session 14: Research Presentations and Discussion
Conducting student research presentations and engaging in discussions from an academic perspective.

Session 15: Comprehensive Discussion and Summary
Reviewing the entire course and systematically organizing methods for evaluating research outcomes.

Remote (Fully Online)

This course will be conducted remotely (fully online).

Limited permission for use

In this course, the use of generative AI is permitted as a supportive tool in processes such as examining research topics, surveying prior studies and related information, and organizing the structure of presentations.
However, students must verify the accuracy and validity of any AI-generated content based on primary sources, including original research articles and official publications.

When generative AI is used in preparing reports or presentation materials, submitting AI-generated outputs verbatim is not permitted.
Students are required to critically review and understand the generated content, organize it appropriately, and describe and explain it in their own words with full responsibility for their research.
If submissions contain factual inaccuracies or insufficiently supported statements due to issues such as hallucinations, the evaluation may be significantly reduced.

No designated textbook. Lecture materials will be provided as needed.

The Craft of Research
Wayne C. Booth, Gregory G. Colomb, Joseph M. Williams, Joseph Bizup, William T. FitzGerald
The Craft of Research, 4th ed., The University of Chicago Press, 2016.
ISBN-13: 978-0226239736
ISBN-10: 022623973X

[Preparatory Study]
Students are expected to survey prior studies and reference materials related to each session’s topic and organize their relevance to their own research. In addition, students should prepare key discussion points and questions in advance of presentations and discussions.
Estimated time: 2 hours per session

[Follow-up Study]
Based on the discussions and feedback provided during class, students should review their research content and presentation materials and reflect these insights in refining their research design and evaluation methods. When necessary, additional literature review and data organization should be conducted.
Estimated time: 2 hours per session

In this course, active learning is implemented primarily through student research presentations, discussions, and peer evaluation.
Through presentations and discussions in each session, students explain their own research and exchange opinions on others’ research based on academic evidence.
This approach deepens students’ understanding of research content and enhances their ability to objectively assess the novelty and validity of research, as well as to engage in constructive academic discussion.

[Evaluation Criteria]
In this course, students are evaluated comprehensively based on their understanding of the research content, the clarity of the novelty and originality of their research, the validity of explanations grounded in evidence, and the quality of their contributions to discussions based on academic reasoning.

[Evaluation Methods]
Final grades are determined based on the following components:
1. Contribution to research presentations and discussions: 50%
Evaluation is based on the clarity and logical structure of research presentations, the level of understanding of the research background, objectives, methods, and results, as well as the appropriateness of comments and the quality of constructive, academically grounded feedback provided on others’ research during discussions.

2. Reports and presentation materials: 50%
Evaluation focuses on whether the research content is systematically organized, whether novelty and originality are appropriately demonstrated, and whether explanations and evaluations are conducted based on evidence such as experimental data and analytical results.
When generative AI is used, particular emphasis is placed on whether the accuracy and validity of the content are verified using primary sources and whether the work is described and explained based on the student’s own understanding.

Assignment and assessment results will be disclosed via the Universal Passport (UNIPA).
Additional feedback may be provided during class sessions or individually, as necessary.

As the course is conducted primarily through research presentations and discussions, students are expected to prepare proactively based on their ongoing research considerations and engage in academically grounded discussions.
All classes will be conducted online; therefore, students must ensure a stable internet connection and an appropriate online environment for presentations and discussions.

Not applicable

This course may be conducted with flexible adjustments to the content and progression, within the scope of the course objectives and learning outcomes described in the syllabus, depending on students’ research topics and levels of understanding.