Conversational AI for Medical Education
UX Design Initiative @ Elsevier
Company
Elsevier
My Role
Co-Design Lead
Timeline
12 weeks (Oct 2023- Dec 2023)
Deliverables
UX/UI Design High-Level Concept Visualization User Research and Persona Development
Description
Elsevier, a leading provider of scientific, technical, and medical information, tasked me with leading the UX design for an innovative AI Conversational Interface aimed at medical students.
Context
The aim of the project was to validate the practical usability and appeal of the conversational interface, ensuring it meets the specific needs of final-year medical students and junior residents. Through comprehensive user research and iterative design processes, we focused on delivering a solution that is both highly desirable and technically feasible.
Deliverables
Contribution
User Research Interface Design Usability Testing Prototyping Co-Project Management
1 Principal UX Designer 1 Senior UX Designer (me) 1 Senior Content 1 System Architect 1 Software Engineer
TEAM
Background
Elsevier considers GenAI chat integration to make medical education more interactive and responsive to student needs.
Challenge
Elsevier's current platform lacks a dynamic GenAI interface for engaging students in interactive clinical case studies.
Goals
Solution
AI-Powered Study Mentor Interface
Developed a prototype integrating an AI conversational interface into the user experience, enabling medical students to interactively navigate clinical case studies with an intelligent GenAI study mentor.
User Research & Findings
To evaluate the potential of a conversational AI interface in enhancing medical education, we engaged in comprehensive user research with our target demographic: final-year medical students and junior residents. Our research aimed to validate the concept's desirability.
Methodology: A Hybrid Approach
We employed a mixed-methods approach, blending qualitative and quantitative research tactics:
In-Depth Interviews: Conducted to capture the nuanced perspectives and expectations of students towards AI in their education.
Surveys: Distributed to gain a broader understanding of current usage patterns of similar technologies.
Key Insights
Current Usage Patterns:
Many students already utilized digital resources, indicating a familiarity and openness to tech-based learning aids.
Popular platforms like Amboss and Pass Medicine provided valuable learning frameworks, setting a high bar for our AI solution.
Desirability and Expectations:
Conversational AI was perceived as highly desirable, primarily for its potential to simulate realistic patient interactions and facilitate clinical decision-making.
Students expected a seamless, proactive experience that would not only respond to queries but also guide their learning journey.
Interaction Preferences:
The type of input varied, with some students preferring concise questions and others leaning towards detailed exploratory discussions.
Responses were expected to be concise yet comprehensive, with an friendly, helpful, and supportive tone and provision of citations where applicable.
The Process
The AIM and the 'How Might We' Questions
Our mission was to create an AI tool loved by students and endorsed by educators, addressing their unique needs in the learning process.
Desk Research and Ideation Workshops
Leveraging insights from our team and existing AI solutions, we crafted four distinct concepts responding to our targeted 'How Might We' questions.
We sketched user profiles and storyboards to visualize AI's potential impact.
Round 1: Unearthing Insights Through User Interviews
Engaging with students unveiled a desire for personalization, simplicity, and integration in AI tools. Contrary to our assumptions, students were open to AI, especially when it's recommended by trusted institutions.
Part 1: Learning from Feedback
We refined our AI mentors based on feedback from Round 1, focusing on what students need, creating intuitive designs, and improving through continuous testing.
Part 2: Designing Conversations
We then developed realistic interactions for the AI mentor, tailored to scenarios like pediatric asthma. This included mapping out conversation flows, scripting medical dialogues, and designing user-friendly interface wireframes.
Round 2: Refinement and User Feedback
We refined our AI models and interfaces for a second round of testing, gathering feedback from students and educators, confirming the appeal of our 'AI Mentor' concept for clinical studies and the 'AI Performance Tool' for classroom enhancement.
Unexpected Insights and Project Evolution
Discovering the complementary potential of both concepts, we saw a path to a unified solution. Though project priorities shifted, our groundwork has become a cornerstone for future endeavors in AI and education.
Top 3 Learnings and Their Impact
Personalization as a Necessity: Adaptive solutions drive engagement and are essential for educational tools.
The Power of Simplicity: Clarity and ease are vital, even in sophisticated AI applications.
The Value of Collaboration: Diverse input and iterative design are the heartbeats of innovation.
Personal Takeaways
Adapting to User Needs
This project emphasized the importance of truly understanding and meeting user needs, reinforcing that successful design hinges on deep user insights.
Value of Iteration
Iterative design proved essential, with each round of feedback refining our approach. This taught me the continuous improvement is critical to achieving effective solutions.
Collaboration's Impact
Collaborating with a diverse team enhanced the design process and expanded my perspective on solving complex challenges through interdisciplinary efforts.
Looking Forward
The insights from this project have deepened my understanding of AI's potential in education. It help become the baseline into the next GenAI product workflow I immediately went into after this project.
I'm eager to apply these lessons to future projects, pushing the boundaries of user engagement and educational outcomes.
Conversational AI for Medical Education
UX Design Initiative @ Elsevier
Company
Elsevier
My Role
Co-Design Lead
Timeline
12 weeks (Oct 2023- Dec 2023)
Deliverables
UX/UI Design High-Level Concept Visualization User Research and Persona Development
Description
Elsevier, a leading provider of scientific, technical, and medical information, tasked me with leading the UX design for an innovative AI Conversational Interface aimed at medical students.
Context
The aim of the project was to validate the practical usability and appeal of the conversational interface, ensuring it meets the specific needs of final-year medical students and junior residents. Through comprehensive user research and iterative design processes, we focused on delivering a solution that is both highly desirable and technically feasible.
Deliverables
Contribution
User Research Interface Design Usability Testing Prototyping Co-Project Management
1 Principal UX Designer 1 Senior UX Designer (me) 1 Senior Content 1 System Architect 1 Software Engineer
TEAM
Background
Elsevier considers GenAI chat integration to make medical education more interactive and responsive to student needs.
Challenge
Elsevier's current platform lacks a dynamic GenAI interface for engaging students in interactive clinical case studies.
Goals
Solution
AI-Powered Study Mentor Interface
Developed a prototype integrating an AI conversational interface into the user experience, enabling medical students to interactively navigate clinical case studies with an intelligent GenAI study mentor.
User Research & Findings
To evaluate the potential of a conversational AI interface in enhancing medical education, we engaged in comprehensive user research with our target demographic: final-year medical students and junior residents. Our research aimed to validate the concept's desirability.
Methodology: A Hybrid Approach
We employed a mixed-methods approach, blending qualitative and quantitative research tactics:
In-Depth Interviews: Conducted to capture the nuanced perspectives and expectations of students towards AI in their education.
Surveys: Distributed to gain a broader understanding of current usage patterns of similar technologies.
Key Insights
Current Usage Patterns:
Many students already utilized digital resources, indicating a familiarity and openness to tech-based learning aids.
Popular platforms like Amboss and Pass Medicine provided valuable learning frameworks, setting a high bar for our AI solution.
Desirability and Expectations:
Conversational AI was perceived as highly desirable, primarily for its potential to simulate realistic patient interactions and facilitate clinical decision-making.
Students expected a seamless, proactive experience that would not only respond to queries but also guide their learning journey.
Interaction Preferences:
The type of input varied, with some students preferring concise questions and others leaning towards detailed exploratory discussions.
Responses were expected to be concise yet comprehensive, with an friendly, helpful, and supportive tone and provision of citations where applicable.
The Process
The AIM and the 'How Might We' Questions
Our mission was to create an AI tool loved by students and endorsed by educators, addressing their unique needs in the learning process.
Desk Research and Ideation Workshops
Leveraging insights from our team and existing AI solutions, we crafted four distinct concepts responding to our targeted 'How Might We' questions.
We sketched user profiles and storyboards to visualize AI's potential impact.
Round 1: Unearthing Insights Through User Interviews
Engaging with students unveiled a desire for personalization, simplicity, and integration in AI tools. Contrary to our assumptions, students were open to AI, especially when it's recommended by trusted institutions.
Part 1: Learning from Feedback
We refined our AI mentors based on feedback from Round 1, focusing on what students need, creating intuitive designs, and improving through continuous testing.
Part 2: Designing Conversations
We then developed realistic interactions for the AI mentor, tailored to scenarios like pediatric asthma. This included mapping out conversation flows, scripting medical dialogues, and designing user-friendly interface wireframes.
Round 2: Refinement and User Feedback
We refined our AI models and interfaces for a second round of testing, gathering feedback from students and educators, confirming the appeal of our 'AI Mentor' concept for clinical studies and the 'AI Performance Tool' for classroom enhancement.
Unexpected Insights and Project Evolution
Discovering the complementary potential of both concepts, we saw a path to a unified solution. Though project priorities shifted, our groundwork has become a cornerstone for future endeavors in AI and education.
Top 3 Learnings and Their Impact
Personalization as a Necessity: Adaptive solutions drive engagement and are essential for educational tools.
The Power of Simplicity: Clarity and ease are vital, even in sophisticated AI applications.
The Value of Collaboration: Diverse input and iterative design are the heartbeats of innovation.
Personal Takeaways
Adapting to User Needs
This project emphasized the importance of truly understanding and meeting user needs, reinforcing that successful design hinges on deep user insights.
Value of Iteration
Iterative design proved essential, with each round of feedback refining our approach. This taught me the continuous improvement is critical to achieving effective solutions.
Collaboration's Impact
Collaborating with a diverse team enhanced the design process and expanded my perspective on solving complex challenges through interdisciplinary efforts.
Looking Forward
The insights from this project have deepened my understanding of AI's potential in education. It help become the baseline into the next GenAI product workflow I immediately went into after this project.
I'm eager to apply these lessons to future projects, pushing the boundaries of user engagement and educational outcomes.
Conversational AI for Medical Education
UX Design Initiative @ Elsevier
Company
Elsevier
My Role
Co-Design Lead
Timeline
12 weeks (Oct 2023- Dec 2023)
Deliverables
UX/UI Design High-Level Concept Visualization User Research and Persona Development
Description
Elsevier, a leading provider of scientific, technical, and medical information, tasked me with leading the UX design for an innovative AI Conversational Interface aimed at medical students.
Context
The aim of the project was to validate the practical usability and appeal of the conversational interface, ensuring it meets the specific needs of final-year medical students and junior residents. Through comprehensive user research and iterative design processes, we focused on delivering a solution that is both highly desirable and technically feasible.
Deliverables
Contribution
User Research Interface Design Usability Testing Prototyping Co-Project Management
1 Principal UX Designer 1 Senior UX Designer (me) 1 Senior Content 1 System Architect 1 Software Engineer
TEAM
Background
Elsevier considers GenAI chat integration to make medical education more interactive and responsive to student needs.
Challenge
Elsevier's current platform lacks a dynamic GenAI interface for engaging students in interactive clinical case studies.
Goals
Solution
AI-Powered Study Mentor Interface
Developed a prototype integrating an AI conversational interface into the user experience, enabling medical students to interactively navigate clinical case studies with an intelligent GenAI study mentor.
User Research & Findings
To evaluate the potential of a conversational AI interface in enhancing medical education, we engaged in comprehensive user research with our target demographic: final-year medical students and junior residents. Our research aimed to validate the concept's desirability.
Methodology: A Hybrid Approach
We employed a mixed-methods approach, blending qualitative and quantitative research tactics:
In-Depth Interviews: Conducted to capture the nuanced perspectives and expectations of students towards AI in their education.
Surveys: Distributed to gain a broader understanding of current usage patterns of similar technologies.
Key Insights
Current Usage Patterns:
Many students already utilized digital resources, indicating a familiarity and openness to tech-based learning aids.
Popular platforms like Amboss and Pass Medicine provided valuable learning frameworks, setting a high bar for our AI solution.
Desirability and Expectations:
Conversational AI was perceived as highly desirable, primarily for its potential to simulate realistic patient interactions and facilitate clinical decision-making.
Students expected a seamless, proactive experience that would not only respond to queries but also guide their learning journey.
Interaction Preferences:
The type of input varied, with some students preferring concise questions and others leaning towards detailed exploratory discussions.
Responses were expected to be concise yet comprehensive, with an friendly, helpful, and supportive tone and provision of citations where applicable.
The Process
The AIM and the 'How Might We' Questions
Our mission was to create an AI tool loved by students and endorsed by educators, addressing their unique needs in the learning process.
Desk Research and Ideation Workshops
Leveraging insights from our team and existing AI solutions, we crafted four distinct concepts responding to our targeted 'How Might We' questions.
We sketched user profiles and storyboards to visualize AI's potential impact.
Round 1: Unearthing Insights Through User Interviews
Engaging with students unveiled a desire for personalization, simplicity, and integration in AI tools. Contrary to our assumptions, students were open to AI, especially when it's recommended by trusted institutions.
Part 1: Learning from Feedback
We refined our AI mentors based on feedback from Round 1, focusing on what students need, creating intuitive designs, and improving through continuous testing.
Part 2: Designing Conversations
We then developed realistic interactions for the AI mentor, tailored to scenarios like pediatric asthma. This included mapping out conversation flows, scripting medical dialogues, and designing user-friendly interface wireframes.
Round 2: Refinement and User Feedback
We refined our AI models and interfaces for a second round of testing, gathering feedback from students and educators, confirming the appeal of our 'AI Mentor' concept for clinical studies and the 'AI Performance Tool' for classroom enhancement.
Unexpected Insights and Project Evolution
Discovering the complementary potential of both concepts, we saw a path to a unified solution. Though project priorities shifted, our groundwork has become a cornerstone for future endeavors in AI and education.
Top 3 Learnings and Their Impact
Personalization as a Necessity: Adaptive solutions drive engagement and are essential for educational tools.
The Power of Simplicity: Clarity and ease are vital, even in sophisticated AI applications.
The Value of Collaboration: Diverse input and iterative design are the heartbeats of innovation.
Personal Takeaways
Adapting to User Needs
This project emphasized the importance of truly understanding and meeting user needs, reinforcing that successful design hinges on deep user insights.
Value of Iteration
Iterative design proved essential, with each round of feedback refining our approach. This taught me the continuous improvement is critical to achieving effective solutions.
Collaboration's Impact
Collaborating with a diverse team enhanced the design process and expanded my perspective on solving complex challenges through interdisciplinary efforts.
Looking Forward
The insights from this project have deepened my understanding of AI's potential in education. It help become the baseline into the next GenAI product workflow I immediately went into after this project.
I'm eager to apply these lessons to future projects, pushing the boundaries of user engagement and educational outcomes.
Conversational AI for Medical Education
UX Design Initiative @ Elsevier
Company
Elsevier
My Role
Co-Design Lead
Timeline
12 weeks (Oct 2023- Dec 2023)
Deliverables
UX/UI Design High-Level Concept Visualization User Research and Persona Development
Description
Elsevier, a leading provider of scientific, technical, and medical information, tasked me with leading the UX design for an innovative AI Conversational Interface aimed at medical students.
Context
The aim of the project was to validate the practical usability and appeal of the conversational interface, ensuring it meets the specific needs of final-year medical students and junior residents. Through comprehensive user research and iterative design processes, we focused on delivering a solution that is both highly desirable and technically feasible.
Deliverables
Contribution
User Research Interface Design Usability Testing Prototyping Co-Project Management
1 Principal UX Designer 1 Senior UX Designer (me) 1 Senior Content 1 System Architect 1 Software Engineer
TEAM
Background
Elsevier considers GenAI chat integration to make medical education more interactive and responsive to student needs.
Challenge
Elsevier's current platform lacks a dynamic GenAI interface for engaging students in interactive clinical case studies.
Goals
Solution
AI-Powered Study Mentor Interface
Developed a prototype integrating an AI conversational interface into the user experience, enabling medical students to interactively navigate clinical case studies with an intelligent GenAI study mentor.
User Research & Findings
To evaluate the potential of a conversational AI interface in enhancing medical education, we engaged in comprehensive user research with our target demographic: final-year medical students and junior residents. Our research aimed to validate the concept's desirability.
Methodology: A Hybrid Approach
We employed a mixed-methods approach, blending qualitative and quantitative research tactics:
In-Depth Interviews: Conducted to capture the nuanced perspectives and expectations of students towards AI in their education.
Surveys: Distributed to gain a broader understanding of current usage patterns of similar technologies.
Key Insights
Current Usage Patterns:
Many students already utilized digital resources, indicating a familiarity and openness to tech-based learning aids.
Popular platforms like Amboss and Pass Medicine provided valuable learning frameworks, setting a high bar for our AI solution.
Desirability and Expectations:
Conversational AI was perceived as highly desirable, primarily for its potential to simulate realistic patient interactions and facilitate clinical decision-making.
Students expected a seamless, proactive experience that would not only respond to queries but also guide their learning journey.
Interaction Preferences:
The type of input varied, with some students preferring concise questions and others leaning towards detailed exploratory discussions.
Responses were expected to be concise yet comprehensive, with an friendly, helpful, and supportive tone and provision of citations where applicable.
The Process
The AIM and the 'How Might We' Questions
Our mission was to create an AI tool loved by students and endorsed by educators, addressing their unique needs in the learning process.
Desk Research and Ideation Workshops
Leveraging insights from our team and existing AI solutions, we crafted four distinct concepts responding to our targeted 'How Might We' questions.
We sketched user profiles and storyboards to visualize AI's potential impact.
Round 1: Unearthing Insights Through User Interviews
Engaging with students unveiled a desire for personalization, simplicity, and integration in AI tools. Contrary to our assumptions, students were open to AI, especially when it's recommended by trusted institutions.
Part 1: Learning from Feedback
We refined our AI mentors based on feedback from Round 1, focusing on what students need, creating intuitive designs, and improving through continuous testing.
Part 2: Designing Conversations
We then developed realistic interactions for the AI mentor, tailored to scenarios like pediatric asthma. This included mapping out conversation flows, scripting medical dialogues, and designing user-friendly interface wireframes.
Round 2: Refinement and User Feedback
We refined our AI models and interfaces for a second round of testing, gathering feedback from students and educators, confirming the appeal of our 'AI Mentor' concept for clinical studies and the 'AI Performance Tool' for classroom enhancement.
Unexpected Insights and Project Evolution
Discovering the complementary potential of both concepts, we saw a path to a unified solution. Though project priorities shifted, our groundwork has become a cornerstone for future endeavors in AI and education.
Top 3 Learnings and Their Impact
Personalization as a Necessity: Adaptive solutions drive engagement and are essential for educational tools.
The Power of Simplicity: Clarity and ease are vital, even in sophisticated AI applications.
The Value of Collaboration: Diverse input and iterative design are the heartbeats of innovation.
Personal Takeaways
Adapting to User Needs
This project emphasized the importance of truly understanding and meeting user needs, reinforcing that successful design hinges on deep user insights.
Value of Iteration
Iterative design proved essential, with each round of feedback refining our approach. This taught me the continuous improvement is critical to achieving effective solutions.
Collaboration's Impact
Collaborating with a diverse team enhanced the design process and expanded my perspective on solving complex challenges through interdisciplinary efforts.
Looking Forward
The insights from this project have deepened my understanding of AI's potential in education. It help become the baseline into the next GenAI product workflow I immediately went into after this project.
I'm eager to apply these lessons to future projects, pushing the boundaries of user engagement and educational outcomes.