AI Cover Letter Generator

Role

Associate Product Designer

Team

1 UX Lead, 1 Product Manager, 2 Developers, 1 Product Designer (myself)

Company

Health eCareers, a job board for healthcare practitioners

Duration

16 weeks (September to December 2023)

This project aims to develop an AI-driven customized cover letter generator for healthcare professionals, including but not limited to Nurse Practitioners, Physicians, Physician Assistants, etc.

  • Created detailed wireframes

  • Crafted high-fidelity interactive prototypes

  • Generated user test script and analyzed testing results

  • Presented prototype to stakeholders, UX employee resource group

"I was surprised by the little amount of effort on my part!"

User test participant, a Nurse Practitioner

1200 visitors in the first 6 months of release

After my contract was over, I reached back out to my Design Manager who let me know that in April, the generator experienced a 175% increase in users, marking the highest growth spike within the first year of release.

What's the problem?

Healthcare job seekers often struggle to write personalized and effective cover letters when applying for jobs. 

In today's competitive job market, crafting a compelling cover letter is crucial for healthcare professionals seeking new opportunities.

Writing a cover letter that stands out is time-consuming and requires a deep understanding of the job requirements and the candidate's skills and experiences. Additionally, it's challenging to balance the need to showcase the candidate's qualifications with a persuasive and professional tone. As a result, job seekers may miss out on job opportunities due to poorly written or generic cover letters. 

How can we solve the issue?

The healthcare industry needs an automated solution for crafting customized cover letters. 

We can use Artificial Intelligence to analyze a healthcare job seeker’s previous experiences and extract relevant skills and qualifications to generate a tailored cover letter for potential employers.

Goals:

  • Streamline the cover letter writing process and save healthcare professionals valuable time

  • Help applicants stand out during their job search

  • Increase chances of getting hired

What would this solution look like? 

Integration with Generative AI

Generative AI was utilized in this product to enhance the personalization and efficiency of creating tailored cover letters. By leveraging advanced AI technologies, the system can generate highly relevant, customized content that aligns with the job seeker's resume and the specific job posting.

User Flow

I mapped out a user flow to understand how a jobseeker might interact with the AI generator and to use it as a guide as I begin the design process. 

Design

I incorporated elements from the current user flow and the design style of Health eCareers pages. I also drew upon my own experience using similar tools, such as online AI resume checkers, to inform the functionality and layout of the generator.

Wireframing

After a round of feedback on my designs with my UX Lead, I leveraged the Health eCareers Design System to refine my prototypes to align with our established brand guidelines and optimize the layout for improved usability. 

Personalization

To provide job seekers with more control over their generated cover letter, this product enables job seekers to create a version that fits their personality and the requirements of the application. It offers several personalization options to to ensure that each cover letter is uniquely tailored to the job seeker's preferences and the specific job posting. Some key features:

  • tailor cover letters to individual job postings

  • offer customization options

  • incorporate user resume information

Integration with Generative AI

Generative AI was utilized in this product to enhance the personalization and efficiency of creating tailored cover letters. By leveraging advanced AI technologies, the system can generate highly relevant, customized content that aligns with the job seeker's resume and the specific job posting.

Prototype Walkthrough Video

Testing

I worked with the UX Lead to run an unmoderated user test on my high-fidelity prototype with 10 participants who were Physician Assistants, Registered Nurses, Obstetrician, and Nurse Practitioners, with job experiences of up to 20 years of experience. 

User Test Questions:

  1. What are the participant’s comfort levels and experiences with AI generators?

  2. Do all the options currently provided in the cover letter generator make sense to job seekers and address their needs?

  3. How would participants interact with the generator? Does it work as expected? What are some things they are satisfied with, and what do they dislike?

Key Insights

Insight 1

Participants find the cover letter generator intuitive and a valuable starting point for their job applications.

Implementation

No design changes were made at this stage. Rather, this helped us understand the product's effectiveness in addressing the original challenge of crafting personalized cover letters.

"I was surprised by the little amount of effort on my part!"

"I was surprised by the little amount of effort on my part!"

User test participant, a Nurse Practitioner

Insight 2

The value of the AI generator is unclear in how it might aid the job seeker's application process.

Implementation

No design changes were made at this stage. However, this feedback informed our efforts to enhance user understanding of the AI-driven features in future iterations of the product.

Insight 3

Participants want to compare different versions of the generated cover letter. However, restarting the process of generating a cover letter is time-consuming, posing a barrier for users.

Implementation

To address this need, we need to allow the user to generate their cover letter in a different style without restarting the entire process. 

I presented these insights to the Product Manager, highlighting user feedback and the proposed solution to address the identified need. After a discussion and approval, the following changes were incorporated into the cover letter generator.

Post-Release Results

After the product was released, I conducted another unmoderated user test to gather evaluative user testing findings from healthcare professionals post-release. Because this was towards the end of my contract, I made the following recommendations for the next designer to iterate on my design:

Finding 1

There is a misunderstanding of the purpose of the cover letter generator, which is to tailor one’s cover letter to a specific job posting.

Recommendation

Add a line of instructions telling users to copy and paste job information from a real job posting they are interested in. 

Finding 2

Participants want to be able to include personalized options within their generated cover letters.

Recommendation

Add an input box allowing users to highlight specific qualities that they believe are important to include in the generated cover letter.

Reflection

  • This was my first contracting role, exposing me to cross-functional teamwork and the emerging field of generative artificial intelligence, a topic I was eager to explore.

  • I learned that designing with generative artificial intelligence involves understanding the technical system before starting any design work. This approach helped me align my design approach with the system's capabilities and limitations.

  • I gained hands-on experience with conducting user research. Some highlights of this experience included crafting test scripts, analyzing interviews, and synthesizing interviews. I learned how to discern valuable feedback, prioritize findings, and transform interview findings into actionable changes, utilizing supporting clips from testers to strengthen my design changes and recommendations. 

  • A challenge I faced was finding the right balance between automation and personalization. It was important to give users the ability to gain ownership over their work, while taking advantage of the abilities of AI. To address this, I added customizing features to tailor each cover letter to individual experiences.

  • I learned how to highlight my product-thinking skills and defend my design decisions to Product leads and Developers. Tthese meetings helped me refine my ability to articulate design rationale and advocate for user needs within a cross-functional team.

  • I learned to present my work frequently, and receive continuous feedback, through presenting my progress on this project within a company-wide UX working group meeting, which was created to bring UX teams together across the various subsidiaries at Everyday Health Group. A screenshot of my presentation is shown below! 

If I had enough time to continue working on this project, I would…

Track metrics such as time on task and conversion rates to identify areas for design improvements in the next iteration

Conduct a longitudinal study to see the correlation between generated cover letters and hiring rates

Try out the cover letter generator ↗

Testing

I worked with the UX lead to run an unmoderated user test on my high-fidelity prototype with 10 participants who were Physician Assistants, Registered Nurses, Obstetrician, and Nurse Practitioners, with job experiences of up to 20 years of experience. 

User Test Questions:

  1. What are the participant’s comfort levels and experiences with AI generators?

  2. Do all the options currently provided in the cover letter generator make sense to job seekers and address their needs?

  3. How would participants interact with the generator? Does it work as expected? What are some things they are satisfied with, and what do they dislike?

Key Insights

Insight 1

Participants find the cover letter generator intuitive and a valuable starting point for their job applications.

Quote from a participant: I was surprised by the little amount of effort on my part!

Implementation

No design changes were made at this stage. Rather, this helped us understand the product's effectiveness in addressing the original challenge of crafting personalized cover letters.

Insight 2

The value of the AI generator is unclear in how it might aid the job seeker's application process.

Implementation

No design changes were made at this stage. However, this feedback informed our efforts to enhance user understanding of the AI-driven features in future iterations of the product.

Insight 3

Participants want to compare different versions of the generated cover letter. However, restarting the process of generating a cover letter is time-consuming, posing a barrier for users.

Implementation

To address this need, we need to allow the user to generate their cover letter in a different style without restarting the entire process. 

I presented these insights to the Product Manager, highlighting user feedback and the proposed solution to address the identified need. After a discussion and approval, the following changes were incorporated into the cover letter generator.

Pre-User Test Designs - Iteration 1

Design iteration 2

Reflection

  • This was my first contracting role, exposing me to cross-functional teamwork and the emerging field of generative artificial intelligence, a topic I was eager to explore.

  • I learned that designing with generative artificial intelligence involves understanding the technical system before starting any design work. This approach helped me align my design approach with the system's capabilities and limitations.

  • I gained hands-on experience with conducting user research. Some highlights of this experience included crafting test scripts, analyzing interviews, and synthesizing interviews. I learned how to discern valuable feedback, prioritize findings, and transform interview findings into actionable changes, utilizing supporting clips from testers to strengthen my design changes and recommendations. 

  • A challenge I faced was finding the right balance between automation and personalization. It was important to give users the ability to gain ownership over their work, while taking advantage of the abilities of AI. To address this, I added customizing features to tailor each cover letter to individual experiences.

  • I learned how to highlight my product-thinking skills and defend my design decisions to Product leads and Developers. Tthese meetings helped me refine my ability to articulate design rationale and advocate for user needs within a cross-functional team.

  • I learned to present my work frequently, and receive continuous feedback, through presenting my progress on this project within a company-wide UX working group meeting, which was created to bring UX teams together across the various subsidiaries at Everyday Health Group. A screenshot of my presentation is shown below! 

Try out the cover letter generator ↗

Post-Release Results

After the product was released, I conducted another unmoderated user test to gather evaluative user testing findings from healthcare professionals post-release. Because this was towards the end of my contract, I made the following recommendations for the next designer to iterate on my design:

Finding 1

There is a misunderstanding of the purpose of the cover letter generator, which is to tailor one’s cover letter to a specific job posting.

Recommendation:

Add a line of instructions telling users to copy and paste job information from a real job posting they are interested in. 

Finding 2

Participants want to be able to include personalized options within their generated cover letters.

Recommendation:

Add an input box allowing users to highlight specific qualities that they believe are important to include in the generated cover letter.

This project aims to develop an AI-driven customized cover letter generator for healthcare professionals, including but not limited to Nurse Practitioners, Physicians, Physician Assistants, etc.

  • Created detailed wireframes

  • Crafted high-fidelity interactive prototypes

  • Generated user test script and analyzed testing results

  • Presented prototype to stakeholders, UX employee resource group

"I was surprised by the little amount of effort on my part!"
- User test participant, Nurse Practitioner

1200 visitors in the first 6 months of release

Reached a maximum of 50 users in a single month

"I was surprised by the little amount of effort on my part!"
- User test participant, Nurse Practitioner

1200 visitors in the first 6 months of release

Reached a maximum of 50 users in a single month

This project aims to develop an AI-driven customized cover letter generator for healthcare professionals, including but not limited to Nurse Practitioners, Physicians, Physician Assistants, etc.

  • Created detailed wireframes

  • Crafted high-fidelity interactive prototypes

  • Generated user test script and analyzed testing results

  • Presented prototype to stakeholders, UX employee resource group

AI Cover Letter Generator

Role

Associate Product Designer

Team

1 UX Lead, 1 Product Manager, 2 Developers, 1 Product Designer (myself)

Company

Health eCareers, a job board for healthcare practitioners

Duration

16 weeks (September to December 2023)

AI Cover Letter Generator

Role

Associate Product Designer

Team

1 UX Lead, 1 Product Manager, 2 Developers, 1 Product Designer (myself)

Company

Health eCareers, a job board for healthcare practitioners

Duration

16 weeks (September to December 2023)

AI Cover Letter Generator

Role

Associate Product Designer

Team

1 UX Lead, 1 Product Manager, 2 Developers, 1 Product Designer (myself)

Company

Health eCareers, a job board for healthcare practitioners

Duration

16 weeks (September to December 2023)

AI Cover Letter Generator

Role

Associate Product Designer

Team

1 UX Lead, 1 Product Manager, 2 Developers, 1 Product Designer (myself)

Company

Health eCareers, a job board for healthcare practitioners

Duration

16 weeks (September to December 2023)