Interaction Design User Research Prototyping & Testing

Typing on TV is broken. We rebuilt the keyboard and proved it.

We designed two on-screen keyboards for smart TVs. What started as a three-week college brief, ended as a 24-participant controlled study and a CHI Late-Breaking Work submission. The winning design cut typing effort by a fifth.

My role
Research lead & interaction designer
Team
3 UXD undergrads
Timeline
3-week brief, then a second round for CHI
Guided by
Dr. Debayan Dhar, IIT Guwahati
Output
CHI 2023 LBW submission
The two novel keyboard designs shown on an OTT content browsing screen
73%
of people we surveyed said they face problems typing on their TV.
24
participants in a controlled, randomised, mixed-method study on a real 50″ smart TV.
19/24
preferred the novel 2-Row Predictive keyboard over the layout they use every day.
−20%
fewer button presses for the same title. 43.6 moves against 54.8.
What I owned
The problem framing and brief, the field study, survey, competitive teardown, synthesis artefacts, ideation and concept selection, study design and protocol, participant recruitment and screening, and qualitative analysis.
What the three of us shared
Both keyboard layouts and their interaction models, the literature review, the test rig and logging setup, session moderation, the statistical analysis, and writing the paper. My teammates built the functional prototypes and the prediction model.
01 The problem, briefly

Everything about a TV got smarter except the way you talk to it.

TVs moved from channels to streaming, and that put search at the centre of the experience. The remote never changed: four directional buttons and a select key. We started with a hunch that this was painful. Then we spent the first days proving it wasn't just us.

01
A survey to size it
73% reported problems typing on their TV. Netflix and Prime dominated usage. People got around the keyboard with voice search, external keyboards and companion apps.
02
A field study to see it
Contextual observation plus interview in people's living rooms. Tasks: enter a username and password, then search for a given title on Netflix and Amazon. We logged environment, posture and remote alternatives.
03
A teardown to bound it
We pulled apart Android TV and Fire TV. Almost every app ships its own keyboard, but nearly all default to the same Square Alphabetic grid. That became our control condition.
Field study documentation: photos of TV setups in homes and the range of remotes and alternative input devices people used
Field study: the environments, and the remotes people actually reach for.
Comparison of on-screen keyboards across Android TV and Amazon Fire Stick applications
Teardown: the same alphabetic grid in every app, placed differently each time.
02 Who we were designing for

Three user groups, one shared frustration.

Empathy mapping split our observations into three groups: homemakers, students and working professionals. Professionals were the sharpest case. High familiarity with technology, high expectations, and the least patience for a slow interface. We designed for them.

“I don't like typing on TV. I often ask my son to do it for me.”
Raymond Rajan, 42 · business manager, Fire TV Stick owner
Redesigned brief
Create a contextual keyboard for streaming applications that improves the experience of working professionals as they navigate through letters while typing on TV.
Persona: Raymond Rajan, 42, business manager, with goals, behavioural patterns and app keyboard ratings
Persona: behaviours, not demographics. One thumb, leaned back, delighted when suggestions appear.
Empathy maps for three identified user groups: homemaker, student and professional
Empathy maps: homemaker, student, professional.
Journey map of a user searching for a film title on Amazon Prime, charting emotional highs and lows
Journey map: searching for one title on Prime. The trough is always in the same place, moving through characters.
03 From insight to opportunity

The pain isn't the typing. It's the travelling.

The survey, field notes and teardown gave us five insights. One of them reframed the whole project. People weren't complaining about entering letters. They were complaining about the distance between them.

01
People look forward to active search results that spare them typing altogether.
02
Slow typing speed and missing or lagging feedback are the loudest complaints.
03
There are no shortcuts anywhere, so nothing gets faster with expertise.
04
Attention stays on the screen, not the remote. People operate the remote blind.
05
Almost every problem happens while navigating to a character, not while choosing it.

Four opportunity areas

Reduce typing
Highlight the most probable suggestions, place them better, and show more of them on screen.
Clarify feedback
Make selection state and cursor position unmistakable, so nobody double-checks their own input.
Add shortcuts
Hold-to-travel on the directional buttons, and direct routes to space, backspace and voice.
Rethink the layout
Row and column counts, character placement, static versus dynamic ordering, overall footprint on screen. This is where we went.
04 Exploration

We explored the remote too, then chose to leave it alone.

Our ideation ran wide. Touchpads on the remote, ring-like scroll wheels, grip buttons on the back, QR-launched phone apps, tangible shortcut keys. We scored every idea against all three user groups. The same objection kept winning: anything that changes the hardware adds cost, adds something to learn, and reaches nobody who already owns a TV.

The constraint we set ourselves
2D on-screen keyboards only, driven by the four directional buttons and the select key every remote already has. If a design needed new hardware, it was out of scope. A solution nobody can ship is not a solution.
Ideation list of primary and secondary ideas with sketches of ring, wheel and grid input concepts
Ideation: everything on the table, rated.
Positives and negatives evaluation of the shortlisted ideas
Positives and negatives for each shortlisted idea.
Pros and cons of each concept assessed separately for homemaker, student and professional groups
The same concepts, judged per user group.
Dynamic virtual keyboard concept showing the alphabet re-ordering by most likely next character after each selection
The concept that survived: re-order the alphabet after every keystroke so the character you probably want next is the closest one to reach.
Animated capture of the 2-Row Predictive keyboard being used to type a title
Figma prototype: the prediction row re-ordering as a title is entered.
05 The designs

Two bets on the same problem, and a control to beat.

If navigating between characters is the cost, there are two ways to cut it: shorten the distance to the next character, or shorten the route to any character. The three-week brief produced the first answer. When we decided to take the work to CHI, we ideated a second time to test the other. Both then ran against the grid that streaming apps already ship.

Control
Square Alphabetic
A 6×6 grid, A–Z then 0–9, with space and backspace beneath. The layout almost every OTT app defaults to. Every character stays in view. But you walk to it one cell at a time.
Novel design A · Round one
2-Row Predictive
A static alphabetic row, plus a dynamic row above it that re-orders after every keystroke. An n-gram and frequency model over a corpus of 7,824 Netflix titles decides what sits closest to your cursor. Long-press to travel.
Novel design B · Round two
Quad-Directional
Ideated months later, for the paper. The alphabet splits into a “+” mapped onto the remote's four directions. From the vertical arm, left and right jump whole sections. From the horizontal arm, up and down do. No walking, only jumps.
Annotated diagram of the three functional prototypes showing dynamic row, alphabetic row, spacebar, search bar and backspace
The three functional prototypes as submitted: (1) dynamic row, (2) alphabetic row, (3) spacebar, (4) search bar, (5) backspace.

Working out the details

Layout was the easy part. State and feedback took the time. How a focused key reads from three metres away. Where the cursor lives. What a long-press does. How you reach numerals and symbols without a second keyboard. How backspace stays reachable from anywhere.

Annotated anatomy of the 2-Row Predictive keyboard, calling out the low-opacity prediction row, dual backspace placement and long-press behaviour
Anatomy: every element justified, including two backspaces.
Interaction states: long press to navigate, hovering and selection states, letter to numeral conversion on long press
Travel, hover and selection states; letters convert to numerals on hold.
Interaction states: special character keyboard, cursor repositioning within the search bar, quick route to voice search
Symbols, cursor repositioning, and a one-move route to voice.
Wireframe iterations of the keyboard concept in position on a content browsing screen
Iterations: how much of the content grid the keyboard is allowed to eat.
Further wireframe iterations of the keyboard placement, tested against real platform layouts
Placement checked against real platform layouts before we committed.
06 Try it yourself

Type one title with four buttons.

This is a working rebuild of all three prototypes. You drive it the way the study drove it: four directions and a select key, nothing else. The move counter is the same measure I logged in the sessions. Type ‘THE WITCHER' on each layout and the difference stops being an argument.

The prototype is a faithful rebuild of the three tested keyboards, with a prediction model trained on a representative corpus of streaming titles.

07 Study design

A design opinion is worth nothing until it survives a study.

Our professor thought the concept was worth more than a course grade, so we built a study around it. I designed the protocol: what to measure, how to keep order effects out, and how to catch first impressions before familiarity wiped them.

Participants
24 smart TV users, aged 18–34, deliberately spread across novice (9), intermediate (8) and expert (7) typing proficiency.
Design
Single-session, within-subjects, mixed method. Every participant used all three keyboards; the order was randomised per participant.
Measures
Task completion time, number of moves, errors (backspace count), SUS after each keyboard, and self-reported preference.
Task
Type the title “THE WITCHER” on each keyboard, after a demonstration and 30 seconds of free interaction.

Apparatus

A 50″ 4K smart TV at 1.5 m height, with the participant seated 2 m away at eye level. Prototypes ran on the TV through Amazon's Web Tester on a Fire TV Stick, driven by the stock remote. Real hardware, real remote, real lean-back distance. We logged every button press, press time and resulting character off the device console, so the move counts are measured, not observed.

Plan diagram of the study setup: participant seated 2 metres from a smart TV running a Fire TV Stick, with facilitator, observer and data logging stations
The setup: participant two metres from the TV, facilitator alongside, observer and logging station behind.
Photograph of a participant seated in front of the smart TV running the prototype keyboard during a session
A session in progress: prototype on the TV, stock remote in hand.

Two phases, on purpose

Phase one showed each keyboard cold, and recorded what people said before they touched it. That was deliberate. For an unfamiliar layout, the first reaction is a real usability signal, and it vanishes the moment someone learns the thing. It mattered here. The design people called “intimidating” on sight was the one they found easiest to pick up.

Phase two was the timed task, followed by SUS and a preference question. Keeping the two apart is what let us report the gap between what a design looks like and what it does.

08 Results

One design won. The other failed usefully.

We ran a one-way ANOVA across all four measures, with pairwise comparisons. 2-Row Predictive beat the incumbent on effort and on satisfaction. Quad-Directional lost. It also told us exactly why, and that's the more interesting half of the finding.

Error bar graph of mean task completion time for the three keyboards
Task completion time. 2-Row Predictive fastest at 22.34 s, against 25.95 s for the grid and 33.23 s for Quad-Directional. F(2,69) = 13.39, p < 0.001
Error bar graph of mean number of moves for the three keyboards
Number of moves. 43.58 against 54.75 and 58.71. A fifth fewer button presses for the same title. F(2,69) = 4.18, p = 0.02
Error bar graph of mean SUS scores for the three keyboards
SUS. 74.58 for 2-Row Predictive and 69.38 for the grid, both above average. Quad-Directional at 53.54, well below. F(2,69) = 17.43, p < 0.001
Keyboard
Time
Moves
SUS
Preferred
Square Alphabetic
25.95s
54.75
69.38
4
2-Row Predictive
22.34s
43.58
74.58
19
Quad-Directional
33.23s
58.71
53.54
1
Overlay scatter diagram plotting task completion time against number of moves for all three keyboards
Time against moves, all three keyboards overlaid.

Why the cross lost

This is the pair of numbers I keep coming back to. Quad-Directional needed about the same number of moves as the grid, 58.7 against 54.75. It still took eight seconds longer. If the presses are equal and the time isn't, the cost isn't navigation. It's search. People simply could not find a letter in a “+”.

That killed the hypothesis, and sharpened the principle behind the winner. Text entry is a visual search followed by a motor action. A layout that optimises the second while wrecking the first comes out behind. The alphabetic row worked because it's linear and everyone already knows the order. The prediction row then shortens the trip without ever making you hunt.

What people said

First impressions were wrong
2-Row Predictive looked “tedious” and “lengthy” before use, and people worried they couldn't see every character at once. After use they reported typing much faster and found the predictions accurate. Quad-Directional read as “intimidating”, then turned out easier than anticipated.
Editing, not entering
Most errors were missed symbols, and the request was consistent: let me move a cursor inside the search bar and fix the one wrong character, instead of deleting everything after it.
Put space in the dynamic row
Switching rows just to type a space broke the rhythm of the prediction row. Seven participants went further: “give us that dynamic row on top of the layout we already know, and keep the rest.”
A hybrid is what users want
The strongest signal we got wasn't one of our designs. Seven people asked for the dynamic prediction row bolted onto the grid they already know.
The finding that mattered most
19 of 24 people preferred a keyboard they had used for under five minutes over the one they use every day. People will absolutely learn a new input method, as long as it visibly saves them effort.
09 The paper

Submitted to CHI 2023 as Late-Breaking Work.

Our paper wasn’t accepted, but I learned a lot from the research process. The abstract below is the version we submitted. If you’d like to know more, drop me an email. Happy to chat!

Abstract
Paper abstract: 2-Row Predictive Keyboard and Quad-Directional Keyboard — an Investigation into Two Novel Text Input Methods for Smart TVs
on-screen keyboard character prediction usability OTT platforms keyboard layout
10 What I took from it

The project taught me to hold a design opinion loosely and a measurement tightly. We were sure Quad-Directional was the clever one. It was the clever one. It also lost. The reason it lost, visual search costs more than motor movement, is worth more to me now than a win would have been.

It also taught me the value of instrumenting a prototype. Logging every button press off the device console is what turned “this feels faster” into a fifth fewer presses.

The constraint we set early, no new hardware, is the decision I'd make again. It's the reason the winning design could ship into an existing app tomorrow.

Credits
A third-year interaction design project at IIT Guwahati, with my batchmates Chaitanya & Pranav, extended into a full study and a CHI Late-Breaking Work submission under the guidance of Dr. Debayan Dhar.
Get in touch

Happy to share more on this.

The study protocol, the full paper, or any of the design decisions behind it.

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