The "Monetizing Emotions: The Power of Generative AI" course aims to empower professionals and organizations to harness the transformative potential of emotional AI and generative technologies. Through a combination of theoretical foundations, ethical frameworks, and hands-on experience, the course seeks to equip participants with the knowledge and skills needed to strategically apply AI in emotionally intelligent ways. By exploring real-world case studies and innovative business models, we will guide participants in understanding how to responsibly monetize emotional AI, drive business growth, and create personalized experiences that resonate with audiences across industries.

In today’s interconnected and technology-driven business environment, leaders must navigate cultural diversity while leveraging digital tools to drive success. This course is essential for professionals aiming to develop adaptive leadership skills that foster collaboration, innovation, and resilience in a globalized digital landscape.
Technological Advancements:
Explore the latest developments in generative AI.
Understand how AI interprets and responds to human emotions, creating new opportunities in technology and business.
Business Strategies:
Learn innovative approaches to leverage emotional insights for business success.
Understand the ethical considerations and responsible practices in utilizing generative AI for commercial purposes.
Personal Discovery:
Embark on a journey of self-discovery as you unravel how your emotions are commodified and transformed into products.
Gain insights into the mechanisms behind personalized advertisements, content recommendations, and user engagement strategies.
Empowerment Techniques:
Acquire practical knowledge on safeguarding your personal data and digital footprint.
Explore strategies to maintain control over your online presence and mitigate potential risks associated with emotional data monetization.
Taking Action:
Receive valuable tips on navigating the evolving landscape of AI-driven emotional monetization.
Discover proactive steps to ensure your digital well-being and make informed choices about your online interactions.

Comprehend the Foundations of Generative AI:
Develop a solid understanding of the principles and underlying mechanisms of generative AI.
Explore the Intersection of Emotions and Technology:
Investigate the ways in which human emotions intersect with the field of technology, uncovering opportunities for innovation and transformation.
Evaluate tech Applications:
Assess the current landscape of technology and identify key applications where generative AI, can be effectively integrated to enhance processes.
Analyze Case Studies:
Analyze real-world case studies, including the utilization of generative AI in scenarios, to gain practical insights into the challenges and successes of its implementation.
Harness Emotional Intelligence in Processes:
Learn how to leverage emotional intelligence within the tech framework, exploring ways to enhance communication, negotiation, and decision-making.

*本学の教育ミッションを具現化する形で設定されています。

1. Understand the capabilities and limitations of generative AI, focusing on emotional responses.
2. Explore ethical considerations and responsible use of AI in leveraging emotional engagement.
3. Develop practical skills in creating content that resonates emotionally using generative AI.
4. Analyze case studies to gain insights into successful applications of emotional AI in various industries.
5. Learn effective monetization strategies and business models for AI-generated emotional content.
6. Cultivate critical thinking skills for assessing the societal impact of emotional AI applications.

教育手法 Teaching Method		% of Course Time
インプット型 Traditional		50 %
参加者中心型 Participant-Centered Learning	ケースメソッド Case Method	30 %
	フィールドメソッド Field Method	20 %
合計 Total		100 %

Before the course begins, participants will receive curated reading materials and case studies to establish foundational insights,
Following the course, participants will develop an assignment to apply key learnings in their professional environments. They will engage in case study analysis to address cross-cultural digital challenges.
The post-course assessment will measure individual progress, complemented by instructor feedback, upon request, to offer insights into leadership strengths and areas for improvement. Participants will complete a course feedback survey to evaluate the program’s effectiveness Required amount of preparation: there will be 7 cases and a final individual report. The cases require at least 2 hours each, Minimum 14 hours.The final report is approximately 10 hours

*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。

講師用内規準拠 Method of Assessment	Weights
コールドコール Cold Call	0 %
授業内での挙手発言 Class Contribution	75 %
クラス貢献度合計 Class Contribution Total	75 %
予習レポート Preparation Report	25 %
小テスト Quizzes / Tests	0 %
シミュレーション成績 Simulation	0 %
ケース試験 Case Exam	0 %
最終レポート Final Report	0 %
期末試験 Final Exam	0 %
参加者による相互評価 Peer Assessment	0 %
合計 Total	100 %

評価の留意事項　Notes on Evaluation Criteria

• Giulio Toscani「Augmented prAIority to Enhance Human Judgement through Data and AI」CRC Press（2025）

Augmented prAIority to Enhance Human Judgement through Data and AI

Evaluation
A student’s final grade in this course will be based on the following weighting:


75% Class Participation
25% Individual final project and cases


Assessment reflects the quality of a student’s active participation in class discussions. Much of a manager’s success depends on communication; therefore, effective oral communication will constitute the student’s grade. Written work should be clear, logical, grammatically correct, spell-checked, persuasive, supported by examples, and backed up by citations for any data, ideas or other content used. It should represent the student’s best effort. To do well on the writing reports, you will need to incorporate and apply the course readings.

A note on Class participation:
Grading class participation is necessarily subjective. However, I try to make it as “objective as possible”. Some of the criteria for evaluating effective class participation include:

1 Is the participant prepared? Do comments show evidence of analysis of the case? Do comments add to our understanding of the situation? Does the participant go beyond simple repetition of case facts without analysis and conclusions? Do comments show an understanding of theories, concepts, and analytical devices presented in class lectures or reading materials?
2 Is the participant a good listener? Are the points made relevant to the discussion? Are they linked to the comments of others? Is the participant willing to interact with other class members?
3 Is the participant an effective communicator? Are concepts presented in a concise and convincing way?


The final course grade for a student will be determined based on the following weight distribution, incorporating three evaluation objective criteria:

Class Participation: 75%

Objective Criteria 1: Engagement Level
This evaluation measures the extent of a student's active engagement in class discussions. Recognizing the crucial role of communication in managerial success, a substantial portion of the student's grade is attributed to effective oral communication. Written assignments are expected to demonstrate clarity, logical coherence, grammatical accuracy, spell-check verification, persuasiveness, supported by examples, and substantiated by citations for data, ideas, or other content. It should embody the student's utmost effort, incorporating and applying insights from course readings.

Class Participation Guidelines:
Participation entails attending all class sessions, completing assigned readings, and actively participating in exercises and discussions. Full attendance throughout on-campus sessions is mandatory, and tardiness or early departure may lead to removal from the class. Grading criteria for effective class participation encompass preparedness, substantive comments, relevance to discussions, peer interaction, and effective communication.

Individual Final Project: 25%

Objective Criteria 2: Analytical Depth
Serving as the culmination of course learnings, the Individual Final Project necessitates students to leverage readings and discussions to present a thorough analysis.

Project Guidelines:
To prevent inappropriate use of ChatGPT and fulfill the course criteria, students must structure their assignments according to the six specified sections provided below. This entails creating a Word document (with a minimum of 2,000 words) that examines and assesses the significant impact of the course subject on the contemporary business landscape. Failure to adhere to these criteria will result in an automatic course failure.

Project Sections:
First of all, think of a real company you want to analyze.
1. Introduction: Briefly outline the significance of the course subject in the contemporary business environment using the real company you choose as an example. Clearly articulate how the real company chosen fits with this course, providing this section with at least two course slide screenshots to explain the fit.
2. Literature Review: Find and mention at least four articles not part of this course (from books, essays, or papers) that talk about the same field or a similar situation as the real company chosen. Make sure to give credit to these sources at the end of your assignment in the “References” section.
3. Selection: Examine how the course concepts helped you select the real company chosen, and describe how this process occurred, providing this section with at least two course slide screenshots.
4. Challenges and opportunities: Identify and discuss challenges and opportunities associated to the business of the real company chosen.
5. Conclusions and Learning Reflections: Summarize key findings and insights, providing concluding remarks on the overall impact of the course subject on your learning, supported by a specific example and by at least three course slide screenshots.
6. References: Ensure proper citation using a recognized citation style (e.g., APA, MLA, Chicago).

Submission Guidelines:
The assessment of the assignment will consider the six sections outlined above, each assessing: the clarity of explanation, writing style, formatting, quality and quantity of visual content, and the feasibility of the project. Do not forget to put your name both on the file name and at the heading of the document

Contextualization Requirement:
Objective Criteria 3: Integration of Course Material
Note: Successful completion of the assignment hinges on contextualization, serving as the primary method to deter improper utilization of ChatGPT and meet course requirements. This entails integrating course-specific components such as personal notes, referenced quotes from lectures or peers, instances from course materials, and visual aids from presentation slides. Failure to contextualize your assignment within the course framework, as per the six-sections criteria, will lead to a failing grade. Previous unsuccessful assignments include those lacking the prescribed six-section structure and those discussing your project without integrating course material.

（実務経験 Work experience）

Refereed Articles

• (2023) The effects of the COVID-19 pandemic for AI practitioners: the decrease in tacit knowledge sharing. Journal of Knowledge Management
• (2023) Cómo el trabajo en remoto está reduciendo el conocimiento implícito. Harvard Deusto Business Review
• (2022) Los mundos virtuales, un nuevo reto para la propiedad industrial de las marcas. Harvard Deusto Business Review
• (2022) The role of reciprocity and reputation in service relationships with Arts organisations. Journal of Services Marketing
• (2018) Arts Sponsorship Versus Sports Sponsorship: Which Is Better for Marketing Strategy?. Journal of Nonprofit & Public Sector Marketing