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The Artemis project develops smartphone-based AI tools that help breeders collect and analyze phenotyping data directly in the field, enabling faster and more accurate crop improvement decisions. To scale, they needed to increasinlgy onboard new users to their tools, procedures involved, without expensive in-person trainings.
In plant breeding, phenotyping refers to measuring observable plant traits—such as yield, height, or disease resistance—to identify the best candidates for breeding. This process involves diverse users, including breeders, extension agents, and untrained enumerators, as well as a range of tools beyond manual forms.
Artemis has developed an AI-powered system comprising a mobile app for capturing images in breeding plots and fields, a vehicle to support image collection, and a dashboard where breeders can review images and access AI-based trait analysis. Because this represents a new way of working, many steps and standards require clear explanation—while also conveying the benefits of faster, more efficient, technology-driven processes. The project has created onboarding materials covering tool access, standard operating procedures for image capture, and the broader use of AI in phenotyping, tailored to different users and formats.
However, there was a general feeling of too many formats and platforms, making it difficult to envision how the approach can scale beyond the current coverage in Tanzania and Uganda. At the same time, management has begun to question the efficiency of the well-received in-person trainings, particularly for field technicians involved in data collection and vehicle assembly.
How might we design onboarding that helps many users independently adopt multiple tools with minimal maintenance?
A co-created strategy for Onboarding - the value of criteria for decision making
Like many research projects, the situation and scaling plans of the program are ever-evolving. So a strategy would need to take this fluidity into account: New users are targeted all the time, and with this new languages, knowledge levels and learning preferences. What stays are goals, not the way the project reaches them. So in order to define a valid strategy, we suggested to look at 3 perspectives and re-evaluate their signals periodically: 1. User reality: What users is the project trying to serve and what is their specific need and skills when they come to the tools? 2. Project requirements: What is the project trying to achieve? E.g. how much resources can it afford to invest in Onboarding materials and Journey management? 3. Onboarding best practices: How might the onboarding tactic minimize friction, prove to be consistently at the side of new users, and communicate value?
Learner Profile for Artemis Technician
In an attempt to organize and prioritize the many insights the team already had on target users, learning needs and objectives/motivations to use the App, we designed Learner Profiles that enhance the team’s existing User Personas with regard to Onboarding: Why and how to new users come to the suite of Artemis tools? What are moments of value creation for them?
We also visualized these in Onboarding Journeys, getting away from the perspective of teaching: what do they need to understand? to the notion of welcoming: what creates joy and value for them?
Individualized AI-powered Onboarding is ready to scale to customer support without the need for in-person trainings
From the analysis of user insights, we learned that Onboarding for Artemis tools should build confidence, ensuring technicians feel capable and in control, while breeders would need to feel they can trust the overall process. It should highlight efficiency by making data collection with the image collection App feel faster and lighter, enabling users to move quickly without sacrificing completeness. At the same time, any Onboarding channel must work reliably in real-world conditions and across repeated cycles, with guidance embedded in tasks and support available through clear re-entry points and refreshers. As a result, the project decided to turn to a AI powered chat bot communicating to each user the information he or she needs, when they need it and in a format that is accessible for them.
An AI-enabled WhatsApp bot meets users on a platform they already use, lowering barriers to adoption across diverse and distributed teams. The bot adapts to each user’s prior knowledge and delivers the right information at the right moment, guiding them through tools and workflows in a conversational, step-by-step way. Connected to the Artemis knowledge database, it provides instant answers, generates onboarding and training paths dynamically, and communicates in multiple languages and formats. By merging onboarding with ongoing support, the bot enables users to learn and troubleshoot independently while significantly reducing the need for continuous human-led training and maintenance.
Learner Profile for Artemis Technician
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— Aline, Design & Communication Lead | Glashaus Innovation
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