Perceptual-Adaptive Learning Modules (PALMs) are web-based learning aid software based on cognitive science principles. PALMs intend to address crucial aspects of learning—such as pattern recognition—that traditional instruction methods have overlooked.

In PALMs, the user classifies novel presentations into appropriate groups, such as diagnoses, according to their underlying features. The adaptive learning algorithms used by PALMs schedule learning items based on the individual’s performance. These algorithms are unique in using both response time and response accuracy as ongoing measures of learning strength.


Target Audience
Medical students and residents
Responsive Web App

Domain Experts x 3
Project Manager x 1
Engineer x 3
Designer x 1 👋
Initial development: 2014


This was initially a pilot product to help the company pivot from K-12 math to medical education. The goal was to build our first medical module.


The core technology and development framework were there when I joined the team. But, many user-facing aspects of the product had issues (e.g., sub-optimal legibility, poor touch-screen usability, ambiguous progress indicators, lack of onboarding, dated visual language, content-related anomalies, discouraging user experience, etc.). Additionally, since the team had been iterating on the concepts for years, the framework had accumulated poor practices and outdated libraries, making solution implementation complicated.

My Role

As the first and only design hire, my role was to partner with our domain expert—to materialize her vision—while working closely with the engineers to redesign the interfaces and address usability issues.

Example Contribution

Design System

To streamline the development process of the future PALM and unify the existing ones, I introduced a new design system that includes many visual and behind-the-scenes patterns.

An icon set for a suite of medical PALMs, in the domain of medicine, based on the med insight design system.

Example Contribution

Standardized Feedback Cues

To ensure the learning feedback cues are universally usable and understandable, and quick to parse

A Brain Regions PALM problem, in the prompt state.

A problem in the correct-response state.

A problem in the incorrect-response state.

A problem in the timed-out state.

Example Contribution

Revised Flow

I revised the general flow and inserted new nodes to provide dedicated real estate for timely hand-holding messaging and way-finding indicators. These inserts host static and dynamic messages that aid users build an accurate mental model of the journey.

The comparison of the original flow with the evolved one.

The learning phase intro screen reflects the status of the training module: unstarted vs. in-progress. The action button and the wording depands on the state.
Progress report intro screen, easing users into their first exposure to progress reports.

The learning (or sequence) finale screen congratulates users for reaching the endpoint.

Example Contribution

Value Proposition Communication

To communicate the technical behind-the-scenes aspects of the product, I co-scripted, edited, and created an explainer video, helping novice users understanding PALMs.

An explainer video on the value proposition of the product.


PALMs have helped many medical students, trainees, and professionals develop deep understanding and automaticity with complex subjects. The efficiency and effectiveness of PALMs in training recognition and interpretation of clinical tests and maintaining this training over many months has been demonstrated in published research[1][2][3][4] and presentations at meetings targeting both clinical specialties and medical education.

  1. Krasne S, Stevens CD, Kellman PJ, Niemann JT. Mastering Electrocardiogram Interpretation Skills Through a Perceptual and Adaptive Learning Module. AEM Educ Train. 2020;5(2):e10454. Published 2020 May 5. doi:10.1002/aet2.10454
  2. Romito BT, Krasne S, Kellman PJ, Dhillon A. The impact of a perceptual and adaptive learning module on transoesophageal echocardiography interpretation by anaesthesiology residents. Br J Anaesth. 2016;117(4):477-481. doi:10.1093/bja/aew295
  3. Rimoin L, Altieri L, Craft N, Krasne S, Kellman PJ. Training pattern recognition of skin lesion morphology, configuration and distribution. Journal of the American Academy of Dermatology.
  4. Krasne S, Hillman JD, Kellman PJ, Drake TA. Applying perceptual and adaptive learning techniques for teaching introductory histopathology. J Pathol Inform. 2013;4:34. Published 2013 Dec 31. doi:10.4103/2153-3539.123991