Hive Learning

Hive Learning is a peer-to-peer learning platform used by organisations to help employees learn from each other (through groups, comments and shared content) rather than from top-down courses. The product's value depends on participation: if learners don't comment, share, or feel the platform speaks to them, the peer-learning model collapses into a passive content dump.

The challenge was that "engagement" is easy to ask for and hard to design — it lives in hundreds of small moments: whether a notification feels worth acting on, whether the tone of the platform sounds like a colleague or a corporation, whether sharing a post feels safe and natural.

As the researcher embedded with the product team, I was asked to investigate where the platform was falling short of that goal and feed user evidence into improvements across the experience:

• Micro-interactions — animations, notifications and transitions that shape how the platform feels.
• Voice and tone — whether the platform's language resonated with its audience.
• Overall ease of use — where users got stuck.
• The comment system — the heart of peer learning.
• The share system — how content spreads through organisations.
• User profiles and admin dashboards — for learners and the people running programmes.

Rather than one big study, I ran a series of focused investigations matched to each area, so findings could feed the team as they worked:

• Micro-interactions: researched user preferences and expectations around notifications, animations and transitions, so engagement mechanics were based on what felt rewarding to users, not on what the platform could technically do.
• Voice and tone: ran surveys and interviews to develop a style guide that made the platform sound approachable and human, aligned with how the audience actually talks and learns.
• Ease of use: usability tests and journey mapping to identify pain points, then worked with designers to implement user-centred solutions.
• Comment system: interviews, observation and feedback analysis to understand how learners commented, responded and discussed — optimising for genuine collaboration rather than performative activity.
• Share system: researched the motivations and expectations behind sharing — what made learners want to share content and invite colleagues — informing intuitive sharing flows for collaboration and content creation.
• Profiles & admin dashboards: interviews, surveys and analysis to optimise interfaces for learners and for the administrators who ran programmes, so both sides of the platform worked smoothly.

The programme of work improved the user experience across the platform and supported increased engagement and collaboration among learners — a more seamless learning experience, with the comment and share flows (the core of peer learning) working the way users actually behave. The voice-and-tone style guide also gave the team a durable asset that kept content consistent long after the project.

Working on a social/peer-learning product taught me that engagement research is really behaviour research: people need to understand the value of participating, feel safe doing it, and be nudged at the right moments. The most useful work came from pairing observation (what users actually did) with self-report (what they said they wanted) — they often disagreed, and the truth was in between.