• Demographics:
• Age: Graduate students, ranging from early 20s to mid-career learners (22-55 years old).
• Education Level: Most learners would be highly educated, holding undergraduate or graduate degrees. They may be pursuing advanced studies in education, cognitive science, or related fields.
• Geography: Learners are likely to be international, with diverse cultural and linguistic backgrounds. The tutor bot must accommodate a global audience.
• Learning Context:
• Discipline: Students are learning about cognitive science, learning theories, and pedagogy. The tutor bot must provide content that reflects advanced knowledge in these areas, particularly How People Learn (HPL) frameworks.
• Learning Objectives:
• Understand the principles of learning science (e.g., metacognition, transfer of knowledge).
• Apply HPL frameworks to real-world educational challenges.
• Analyze and reflect on case studies or theories using cognitive science insights.
• Demonstrate deep understanding of learner-centered practices and pedagogy.
• Learning Preferences:
• Diverse Learning Styles: Given the focus on learning theories, many students may appreciate content delivered in multiple formats (text, visuals, interactive models) to match their preferred modes of learning.
• Autonomous and Self-Directed: Learners are likely motivated, self-directed, and capable of engaging in critical thinking. They may expect the tutor bot to allow for flexible, independent learning, offering personalized paths and challenges.
• Deep Engagement with Content: Learners will want the tutor bot to go beyond superficial learning. They expect the bot to provide in-depth, nuanced explanations, perhaps drawing on examples from educational research or empirical studies.
• Technology Comfort:
• Technologically Proficient: Many learners, especially those in professional settings, are likely comfortable with digital tools, platforms, and online learning environments.
• Experience with Educational Technologies: As students in an education-related course, they may be critical of poorly designed technology. They will expect a seamless, intuitive user experience, and they may have prior knowledge of tutor bots or other educational technologies. Many, if not most, have ample experience in using commercial or public LLMs in their studies and daily life.
• Challenges:
• Some learners might struggle with complex theories in cognitive science and would need the bot to break down difficult concepts into digestible pieces.
• Others may demand that the bot challenge them more deeply, asking critical questions or offering higher-order tasks like problem-solving and synthesis.
• Demographics:
• Age and Experience: Likely experienced educators and researchers in the fields of cognitive science and pedagogy. Many may hold advanced degrees (PhDs) and have published research on learning theories or instructional design.
• Needs:
• Supplementary Tool for Teaching: Instructors may see the tutor bot as a tool to augment their teaching, offering students additional support outside class hours. They will expect the bot to provide value without replacing or diminishing their role as educators.
• Assessment of Learner Progress: Instructors will want the bot to provide data-driven insights into how students are progressing through the course, identifying areas where they may need intervention or further explanation.
• Research Insights: As researchers, instructors may want to analyze the interactions between students and the bot for insights into learning behavior, engagement, and effectiveness of educational technologies.
• Expectations for the Tutor Bot:
• Instructors will likely expect the bot to adhere to the highest standards of pedagogical design, ensuring that it reflects research-based best practices in education. The bot must not only support HPL principles but demonstrate exemplary applications of them.
• Instructors might also expect the bot to handle logistical aspects, such as personalized feedback or timely reminders for assignments and deadlines.
• Challenges:
• Instructors will be critical of any shallow engagement with cognitive science concepts. They will expect the tutor bot to be both academically rigorous and pedagogically sound, effectively applying theories like metacognition and transfer in the design.
• Demographics:
• Administrative and IT staff: Individuals responsible for overseeing educational technology implementations within the university.
• University Leadership: Decision-makers at HGSE who support innovation in teaching and learning.
• Needs:
• Scalability and Sustainability: Administrators will be interested in whether the tutor bot can be scaled to serve larger numbers of students across different courses. They will also want to know if the system can be maintained easily and integrated into existing learning management systems, Canvas.
• Data Security and Privacy: Stakeholders will prioritize ensuring that any data collected by the tutor bot, especially student interactions and assessments, is handled securely and complies with institutional policies on privacy and data protection (e.g., FERPA in the US).
• Innovation in Teaching and Learning: Leadership at Harvard will want to see how the tutor bot fits into the broader mission of the university to lead in educational innovation. They may be interested in its potential for interdisciplinary applications beyond just the HPL course.
• Challenges:
• Institutional stakeholders may require strong evidence of the bot’s impact on learning outcomes before committing to its broader adoption. They will need to see demonstrable improvements in student engagement, learning, or satisfaction compared to traditional methods.
• Personalization: Different students may have different levels of familiarity with learning theories, so the bot should offer personalized content and scaffold learning based on student progress.
• Flexibility: The bot should provide flexible learning pathways, allowing students to explore topics of interest in more depth or receive more support where necessary.
• Data-Driven Feedback: Both students and instructors would benefit from detailed feedback based on learning analytics, enabling real-time adjustments to the learning process.
• Accessibility: The bot should be designed with accessibility in mind, ensuring that it is usable by all students, regardless of their physical, cognitive, or technological limitations.