Artificial intelligence could revolutionize conventional eye-tracking technology. Developers would benefit in several ways from incorporating it into accessibility testing. Could this predictive alternative replace today’s standard techniques? What Is AI-Powered Predictive Eye Tracking? AI-powered predictive eye-tracking is an advancement in conventional eye-tracking technologies. It uses a machine learning model to predict where people will look. Its purpose is to use their fixations and scan paths — where their gaze settles and how it jumps from point to point — to inform development processes and improve accessibility testing. There are several ways AI-powered predictive eye tracking can work. The first involves a massive training dataset, where engineers feed tens of thousands of data points to an algorithm. Typical references include attention heatmaps — a visual representation of fixation depicted using cool and warm colors — and past research on how users respond to factors like layout, orientation, color, size, and shape. Another approach involves simulations. Since neural networks are designed to behave like a human brain, they can accurately imitate fixation and scan paths. Alternatively, AI engineers can use a machine learning model to develop synthetic attention heatmaps. Generally, as long as the training data is relevant and preprocessed, their results will be realistic. Professionals can also embed an advanced algorithm into a camera to capture individuals’ eye movements as they scan an image, video, website, or app. These computer vision systems interpret visual data in real-time to determine fixation and scan paths. It can use near-infrared projection or track how the pupil moves in relation to the sclera — the eye’s white outer area. The point of AI-powered predictive eye tracking in accessibility testing is to make platforms as accessible as possible. While many developers account for those with common, permanent visual impairments, they often overlook others. Approximately 15% of the global population — over one billion people — experience situational disabilities. For example, they may have cataracts, night blindness, or broken limbs. With AI, everyone can be accommodated. How Is It Different from Conventional Eye Tracking? Conventional eye-tracking technology differs from AI-powered predictive eye-tracking in that it uses the pupil-center corneal reflection technique. A specialized camera and projection system sends near-infrared light into the user’s eyes. It uses the resulting reflection on the cornea — the transparent, outermost part of the eye that covers the iris and pupil — to establish their relative position. This way, it can measure where an individual’s eyes move. AI-powered systems don’t need to project near-infrared patterns because the algorithms can interpret visual information when embedded into computer vision systems. They can train on existing research to uncover hidden trends. Conversely, conventional eye-tracking technology uses basic mathematical algorithms to calculate fixations and scan paths. Conventional tools are generally inaccurate because someone must manually synthesize eye movement data points into aggregated information blocks. Some information is lost, miscategorized, or misinterpreted in the process, muddling intent. Using these findings for forecasts does not always return precise results. In accessibility testing, the little details matter. For people with disabilities, whether a platform is intuitive is often the difference between being able to use it and having to find an alternative. Since AI has predictive capabilities, it is the clear choice for advanced eye-tracking. Use Cases for AI-Powered Predictive Eye Tracking Several use cases for AI-powered predictive eye tracking in accessibility testing exist. Cursor Control Some people with disabilities lack full use of their limbs or are incapable of fine motor control. With AI-powered predictive eye tracking, they can fully participate in accessibility testing by moving the cursor with their eyes. To click, they can blink twice in rapid succession, fix their gaze on one spot for a predetermined amount of time, or press a hand-held switch. Gaze Prediction Gaze prediction uses past eye movement information to forecast where people will look and what will interest them. These algorithms can analyze behavioral information within seconds — before the user realizes they’re about to make a decision. These real-time adjustments help make the user experience (UX) more intuitive. Personalized Learning Because of the high potential for personalized learning that AI-powered tools provide, experts expect the education sector to play a large role in AI’s growth, with the market projected to be worth $1,345 billion by 2030. Specifically, AI-powered predictive eye tracking can transform accessibility in educational settings, allowing students of all abilities to engage more fully in the learning experience. This technology will let students navigate digital learning resources using only their gaze, fostering an inclusive environment for learners who might otherwise struggle to keep up with their peers. Intuitive UI During accessibility testing, developers can use AI-generated heatmaps to forecast what users will focus on during sessions. Since eye tracking reveals unconscious design preferences, they can use this information to eliminate user interface bloat, ensuring the UX is as streamlined and intuitive as possible. Video Captioning Basic video captioning models translate visual input into language, transcribing videos for people who are deaf, hard-of-hearing or cognitively impaired. This technology is often unavailable or inaccurate. Predictive AI outperforms those transcriptions, whether embedded into an eye-tracking camera or a mounted fisheye lens that monitors on-screen images. Advantages of Integrating Predictive Eye Tracking Into Accessibility Testing The primary benefit of integrating this AI technology into accessibility testing is the reduced hardware costs. Since these systems don’t need to project near-infrared patterns or use specialized cameras, building them is more affordable. Moreover, since algorithms can interact with multiple users simultaneously, developers only need one model for all analyses. Another one of the major advantages of AI is precision. Unlike conventional eye-tracking tools that need information aggregated into strict, predefined blocks, it can account for numerous variables. People with disabilities often scan differently. For example, those with autism have fewer gaze fixations, and those with attention-deficit/hyperactivity disorder have relatively unpredictable eye movements. An advanced algorithm eliminates guesswork by detecting subtle patterns, enabling it to build accurate, personalized profiles for various user segments. This way, developers can tweak their UI and UX with unparalleled precision. With this level of granularity, accessibility testing becomes far more insightful and actionable. Challenges of Integrating Predictive Eye Tracking Into Accessibility Testing Like any emerging technology, AI predictive eye tracking faces some obstacles. For one, developers must consider the legality of using this information to train or inform their model. If they operate in or serve users in the European Union, they must adhere to the General Data Protection Regulation. This may add to development complexity, delaying time to completion. Privacy is crucial for compliance and reputation preservation. One potential solution is to store fixations and scan paths — using the x and y-axis to standardize data points — locally instead of on cloud servers. Anonymizing the data this way protects the users during accessibility testing and future use. Training is another issue. The model may manifest unexplainable biases or skew results if a training dataset is irrelevant or inaccurate. For example, if the references mainly consist of white individuals, it may offensively direct people with epicanthic folds to open their eyes. Companies must factor in all eye, skin, and lighting types for accessibility testing to be precise. Large resource utilization is also a potential issue. The more complex these algorithms are, the more memory and processing power users need. Fortunately, researchers have already begun exploring solutions. One research group developed a saliency prediction model that runs over four times faster and is 93.27% smaller than state-of-the-art alternatives. The Future of AI Eye Tracking in Accessibility Testing Since AI is still a relatively new technology, developers and engineers will incrementally improve it as they explore new use cases and address past pain points. While it may not replace standard near-infrared eye-tracking technology just yet, it likely will in the near future. Artificial intelligence could revolutionize conventional eye-tracking technology. Developers would benefit in several ways from incorporating it into accessibility testing. Could this predictive alternative replace today’s standard techniques? What Is AI-Powered Predictive Eye Tracking? AI-powered predictive eye-tracking is an advancement in conventional eye-tracking technologies. It uses a machine learning model to predict where people will look. Its purpose is to use their fixations and scan paths — where their gaze settles and how it jumps from point to point — to inform development processes and improve accessibility testing. There are several ways AI-powered predictive eye tracking can work. The first involves a massive training dataset, where engineers feed tens of thousands of data points to an algorithm. Typical references include attention heatmaps — a visual representation of fixation depicted using cool and warm colors — and past research on how users respond to factors like layout, orientation, color, size, and shape. Another approach involves simulations. Since neural networks are designed to behave like a human brain, they can accurately imitate fixation and scan paths. Alternatively, AI engineers can use a machine learning model to develop synthetic attention heatmaps. Generally, as long as the training data is relevant and preprocessed, their results will be realistic. Professionals can also embed an advanced algorithm into a camera to capture individuals’ eye movements as they scan an image, video, website, or app. These computer vision systems interpret visual data in real-time to determine fixation and scan paths. It can use near-infrared projection or track how the pupil moves in relation to the sclera — the eye’s white outer area. The point of AI-powered predictive eye tracking in accessibility testing is to make platforms as accessible as possible. While many developers account for those with common, permanent visual impairments, they often overlook others. Approximately 15% of the global population — over one billion people — experience situational disabilities. For example, they may have cataracts, night blindness, or broken limbs. With AI, everyone can be accommodated. 15% of the global population 15% of the global population How Is It Different from Conventional Eye Tracking? Conventional eye-tracking technology differs from AI-powered predictive eye-tracking in that it uses the pupil-center corneal reflection technique. A specialized camera and projection system sends near-infrared light into the user’s eyes. It uses the resulting reflection on the cornea — the transparent, outermost part of the eye that covers the iris and pupil — to establish their relative position. This way, it can measure where an individual’s eyes move. AI-powered systems don’t need to project near-infrared patterns because the algorithms can interpret visual information when embedded into computer vision systems. They can train on existing research to uncover hidden trends. Conversely, conventional eye-tracking technology uses basic mathematical algorithms to calculate fixations and scan paths. Conventional tools are generally inaccurate because someone must manually synthesize eye movement data points into aggregated information blocks. Some information is lost, miscategorized, or misinterpreted in the process, muddling intent. Using these findings for forecasts does not always return precise results. In accessibility testing, the little details matter. For people with disabilities, whether a platform is intuitive is often the difference between being able to use it and having to find an alternative. Since AI has predictive capabilities, it is the clear choice for advanced eye-tracking. Use Cases for AI-Powered Predictive Eye Tracking Several use cases for AI-powered predictive eye tracking in accessibility testing exist. Cursor Control Some people with disabilities lack full use of their limbs or are incapable of fine motor control. With AI-powered predictive eye tracking, they can fully participate in accessibility testing by moving the cursor with their eyes. To click, they can blink twice in rapid succession, fix their gaze on one spot for a predetermined amount of time, or press a hand-held switch. Gaze Prediction Gaze prediction uses past eye movement information to forecast where people will look and what will interest them. These algorithms can analyze behavioral information within seconds — before the user realizes they’re about to make a decision. These real-time adjustments help make the user experience (UX) more intuitive. Personalized Learning Because of the high potential for personalized learning that AI-powered tools provide, experts expect the education sector to play a large role in AI’s growth, with the market projected to be worth $1,345 billion by 2030. Specifically, AI-powered predictive eye tracking can transform accessibility in educational settings, allowing students of all abilities to engage more fully in the learning experience. expect the education sector to play expect the education sector to play This technology will let students navigate digital learning resources using only their gaze, fostering an inclusive environment for learners who might otherwise struggle to keep up with their peers. Intuitive UI During accessibility testing, developers can use AI-generated heatmaps to forecast what users will focus on during sessions. Since eye tracking reveals unconscious design preferences , they can use this information to eliminate user interface bloat, ensuring the UX is as streamlined and intuitive as possible. reveals unconscious design preferences reveals unconscious design preferences Video Captioning Basic video captioning models translate visual input into language, transcribing videos for people who are deaf, hard-of-hearing or cognitively impaired. This technology is often unavailable or inaccurate. Predictive AI outperforms those transcriptions , whether embedded into an eye-tracking camera or a mounted fisheye lens that monitors on-screen images. AI outperforms those transcriptions AI outperforms those transcriptions Advantages of Integrating Predictive Eye Tracking Into Accessibility Testing The primary benefit of integrating this AI technology into accessibility testing is the reduced hardware costs. Since these systems don’t need to project near-infrared patterns or use specialized cameras, building them is more affordable. Moreover, since algorithms can interact with multiple users simultaneously, developers only need one model for all analyses. Another one of the major advantages of AI is precision. Unlike conventional eye-tracking tools that need information aggregated into strict, predefined blocks, it can account for numerous variables. People with disabilities often scan differently. For example, those with autism have fewer gaze fixations , and those with attention-deficit/hyperactivity disorder have relatively unpredictable eye movements. have fewer gaze fixations have fewer gaze fixations An advanced algorithm eliminates guesswork by detecting subtle patterns, enabling it to build accurate, personalized profiles for various user segments. This way, developers can tweak their UI and UX with unparalleled precision. With this level of granularity, accessibility testing becomes far more insightful and actionable. Challenges of Integrating Predictive Eye Tracking Into Accessibility Testing Like any emerging technology, AI predictive eye tracking faces some obstacles. For one, developers must consider the legality of using this information to train or inform their model. If they operate in or serve users in the European Union, they must adhere to the General Data Protection Regulation. This may add to development complexity, delaying time to completion. Privacy is crucial for compliance and reputation preservation. One potential solution is to store fixations and scan paths — using the x and y-axis to standardize data points — locally instead of on cloud servers. Anonymizing the data this way protects the users during accessibility testing and future use. Training is another issue. The model may manifest unexplainable biases or skew results if a training dataset is irrelevant or inaccurate. For example, if the references mainly consist of white individuals, it may offensively direct people with epicanthic folds to open their eyes. Companies must factor in all eye, skin, and lighting types for accessibility testing to be precise. model may manifest unexplainable biases model may manifest unexplainable biases Large resource utilization is also a potential issue. The more complex these algorithms are, the more memory and processing power users need. Fortunately, researchers have already begun exploring solutions. One research group developed a saliency prediction model that runs over four times faster and is 93.27% smaller than state-of-the-art alternatives. runs over four times faster runs over four times faster The Future of AI Eye Tracking in Accessibility Testing Since AI is still a relatively new technology, developers and engineers will incrementally improve it as they explore new use cases and address past pain points. While it may not replace standard near-infrared eye-tracking technology just yet, it likely will in the near future.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

AI-Powered Predictive Eye Tracking Highlights the Future of Accessibility Testing

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