AI for DEI in Online Education

AI for DEI in Online Education

AI for DEI in Online Education

© Honorlock 2024

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AI for DEI in Online Education

AI is a supplement, not a fix.

It can manage big data, not nuanced classroom dynamics.

It can predict academic success, not truly inspire learners.

It can automate communications, not build meaningful relationships.

It can recommend policies, not advocate for change.

It can help educators, not replace them.

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AI for DEI in Online Education

AI is a reflection of you.

From the data it’s fed and rules it follows, to the tasks it performs and usage policies, AI is an extension of the people behind it.

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AI for DEI in Online Education

Table of Contents

AI for DEI in Online Education Part 1: AI Tools for DEI

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Part 2: AI to Develop Equitable Admissions

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Part 3: How to Build Accessible Online Courses

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AI for DEI in Online Education

Part 1:

AI Tools for DEI Educators have used AI for years to automate tasks, spot trends in data, and even help personalize the learning experience.

These foundational tools are beneficial, but the landscape of AI is developing rapidly.

New AI tools are popping up every day with a growing number of features— many that we didn’t even realize we needed.

When adapted and used correctly, they provide more opportunities to transform online education, especially when it comes to supporting and celebrating diversity, equity, inclusion, and accessibility.

While some of the AI tools you’ll learn about in this section aren’t widely used in online learning (yet), they can and should be. We’ll show you how they work and discuss how they can be adapted for online learning environments.

Responsibly integrating AI in online education starts with recognizing its limitations, such as biases, and committing to ongoing efforts to ensure ethical use. These AI tools need to be used purposefully and with full transparency. This requires a deep understanding of how the AI works, its training processes, the data it collects, how that data is protected, and open communication with learners. Most importantly, the value of human oversight cannot be overstated. It ensures that the nuances and complexities of individuals and situations, which AI might overlook, are taken into consideration along with other contextual factors.

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Emotion AI Emotion AI, also called sentiment analysis and affective computing, identifies emotions by analyzing emotional signals in text, audio, and video, and associating them with specific emotions.

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Text: analyzes written language to understand the sentiment and emotional tone of the text content.

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Voice (audio): listens for vocal characteristics (tone, volume, and speed) to determine emotional states.

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Video: determines emotional states based on facial expressions, gestures, and other movements.

Each person expresses emotions differently Emotion AI can provide valuable insights, but it isn’t foolproof or perfect. Here are key considerations to help use it effectively and ethically: Physical conditions or disabilities may limit facial expressions or cause unintended body movements, which could give inaccurate feedback on emotional states.

Cultural differences can impact behavior, gestures, speech, and more. For example, eye contact is a sign of interest in some cultures while other cultures avoid eye contact as a sign of respect.

Openly communicate with learners about its purpose, the types of data collected, how it is analyzed, and how data privacy is protected.

Awareness of how the emotion AI is trained is necessary to minimize biases related to race, gender, or other characteristics.

Human oversight is required to ensure that the nuances and complexities of human emotions are taken into consideration with other context the AI can’t account for.

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Text Emotion AI Text emotion AI analyzes text to determine emotions based on two primary approaches: lexicon (our vocabulary) and machine learning: •

Lexicon-based sentiment analysis: compares the text to a database of keywords and phrases associated with specific emotions and considers surrounding linguistic elements to understand the context.

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Machine learning: compares the text to large collections of text that it was trained on (and continually learns from) to recognize patterns and context. This goes beyond simple keyword matching, as seen in the lexicon-based approach.

Examples of how to use text emotion AI in online courses: •

Identify where learners are struggling: detecting signs of confusion or frustration in written submissions, like discussion responses, can help provide timely support that addresses learners’ needs.

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Personalize feedback: understanding the emotional context of learners’ writing can help educators provide more personalized, empathetic, and supportive feedback in some cases.

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Understand course feedback in-depth: analyzing learners’ course feedback can provide a deeper understanding of their experiences and feelings about the course, which can be used to refine course content and activities.

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Voice Emotion AI Voice emotion AI analyzes vocal characteristics, like tone, pitch, speed, pauses, and enunciation, to better understand the person’s emotional state.

Organizations often use voice emotion AI to provide customer service employees with real-time insights that indicate when customers are growing frustrated, confused, and angry.

If integrated and used appropriately, it can offer educators similar insights during an online course about learners’ frustration, confusion, interest, or disinterest.

Benefits and uses of voice emotion AI in online courses: •

Presentation feedback: providing learners with in-depth feedback on their presentations based on pacing, volume, pitch, and other vocal qualities.

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Adapt course activities based on interest: analyzing vocal characteristics such as tone and intensity can help gauge interest levels in real-time during a lecture, allowing instructors to switch to more interactive activities if disinterest is detected to re-engage learners.

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Identify signs of stress and anxiety: detecting stress and anxiety through vocal characteristics allows educators to immediately intervene and offer support.

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Challenges for Voice Emotion AI Subjectivity Interpreting speech is tricky and it can be difficult to accurately identify emotions. For example, yelling is usually a sign of anger, but it can also express happiness.

Slang It can be difficult for this type of AI to recognize and interpret slang words and phrases if it has yet to be trained on them.

Did you know?

94% of Americans use slang at times Preply, 2022 survey

Dialect and accents Dialect and accents can impact the accuracy of voice emotion AI because they change speech patterns, pronunciation, and even the meaning of words in the same language.

These challenges highlight the importance of training voice emotion AI on diverse datasets that include a wide range of accents, dialects, and cultural speech patterns. This training helps the system become more adaptable and accurate in interpreting emotions from speech with different accents.

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Video Emotion AI Video emotion AI uses a combination of technologies to analyze and understand facial expressions and body language, like posture, gestures, and eye contact.

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Computer vision AI: helps computers see the world as humans do by analyzing and interpreting the environment and objects around them.

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Facial expression recognition software: detects facial expressions and microexpressions by analyzing facial landmarks.

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Body language & gesture recognition: identifies emotions based on body movements and gestures. For example, touching the face can indicate stress and tilting the head can indicate confusion.

Examples of how to use video emotion AI in online courses: •

Understand and accommodate nonverbal communication: helps those with conditions and disabilities communicate through gestures, for example.

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Measure engagement: detects signs of disinterest and confusion, for example, letting educators tailor course activities in real time.

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Nonverbal communication: helps those with specific conditions and disabilities communicate through gestures, for example.

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AI for Tutoring and Support Whether intentionally built for tutoring or adapted, AI tools can help create genuine tutoring experiences similar to one-on-one tutoring sessions. These tools offer a tailored experience and real-time feedback, which is crucial for learners with intellectual disabilities because it helps them make connections between their work and the instructor’s feedback.*

Intelligent Tutoring System (ITS) ITS simulates one-on-one human tutoring by immediately offering personalized feedback and adapting course content based on each learner’s needs, performance, preferences, and learning styles.

It can guide learners through problem-solving steps, offer hints, break down complex topics, and recommend additional content in various formats like text, multimedia, simulations, and game-based learning.

While an ITS can benefit any subject, it’s particularly beneficial for subjects like math, which require—for the most part—a lot of repetition. Itit gives learners progressively challenging tasks based on what they need to learn next. This ensures that learners engage with the right materials at the right difficulty level at the right time.

Reynolds, T., Zupanick, C.E., & Dombeck, M. (2013). Effective Teaching Methods for People with Intellectual Disabilities.

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Here’s how an ITS could work for an algebra course: •

The ITS starts by assessing a learner’s understanding of basic algebraic concepts and their ability to solve problems.

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After assessing knowledge, the ITS customizes learning activities and offers additional content based on the learner’s performance on the initial assessment.

•

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If the learner struggles in a specific area, the ITS provides extra help, like stepby-step explanations and practice problems.

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If the learner excels in a topic, the ITS gradually progresses to more advanced concepts and activities.

The ITS provides immediate feedback and additional context, allowing learners to recognize errors and learn to correct them.

Chatbots Chatbots can offer interactive tutoring that breaks down complex subjects, digs deeper into specific topics, and provides instant answers and feedback.

Learners can use chatbots for tutoring needs, such as: •

Interactive learning activities: engages learners with real-time activities like Q&A sessions, role-playing such as real-time debates, and memorization activities with flashcard-like drills.

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Instant feedback and response: provides feedback on a variety of assignments, like long or short-form essays, and responds instantly to questions.

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Language practice: helps improve language by practicing writing and conversing in real-time, building vocabulary, improving grammar, and translating content.

Roschelle, J., Feng, M., Murphy, R., & Mason, C. (2016). Online Mathematics Homework Increases Student Achievement.

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AI for DEI in Online Education

AI Writing Assistants AI writing assistants like Grammarly and Quill have evolved from spotting punctuation and spelling errors to suggesting improvements to writing style, clarity, organization, and tone. These tools benefit people writing in a second language and individuals with learning disabilities by boosting confidence and delivering feedback and corrections in different formats.

AI writing assistants improve DEI in online courses by: •

Bridging language gaps: helps learners with language barriers or learning disabilities polish their written work, ensuring their ideas are clearly articulated.

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Offering immediate feedback: checks writing instantly and provides real-time feedback in different formats.

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Providing feedback in various formats: offers indirect feedback (underlines errors without corrections) and direct feedback (shows errors with corrections).

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AI for Languages & Culture Translation tools and game-based platforms like Duolingo can help reduce language barriers. Still, they’re just the tip of the iceberg when it comes to languagerelated AI tools, which have many nuances, like localization and cultures.

These AI tools can help build a sense of inclusion and belonging in online education by improving communication, increasing trust, and offering learners different ways to express themselves.

Did you know?

Non-native accents are viewed as less trustworthy by native speakers1 Unfortunately, the unique differences in our speech, like accents and pronunciation, and our writing, such as word choice and spelling, can lead to biases. These biases, whether intentional or not, become associated with perceived intelligence, education, and abilities.2 And when non-native English speakers fall behind or score poorly on tests, educators may assume they have learning disabilities, which is inherently problematic because the real issue—a language barrier—isn’t addressed.3

1

Why don’t we believe non-native speakers? The influence of accent on credibility by Shiri Lev-Ari & Boaz Keysar, April 2010

2

Speaking up: accents and social mobility by Erez Levon, Devyani Sharma, Christian Ilbury. (2022, November).

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Bias against languages other than English hurts students by Dr. Alba Ortiz. (2009)

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Sign Language Interpretation AI Sign language interpretation AI facilitates real-time translation of spoken language and text into sign language, and vice versa.

This technology makes content accessible to deaf or hard-ofhearing learners.

Recent advancements in sign language interpretation AI have led to the creation of realistic videos.

The AI splices together videos and images of real people signing, moving away from cartoon-like avatars. This helps enhance clarity and comprehension, offering learners a more authentic and engaging experience.

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“AI can help those with disabilities gain the confidence they need in order to thrive in online education and in life. It allows them to get their message across clearly and improves the way they are able to express themselves.”

- CollabFWD

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Name Pronunciation AI If your name is unique (and sometimes, even when it isn’t) you’ve probably heard it mispronounced. Unfortunately, many people have come to accept their name being mispronounced and they answer to various mispronunciations. But they shouldn’t have to, especially when there’s name pronunciation AI that can help.

Here’s how it works: Integrates within your platforms AI name pronunciation tools integrate within the LMS, SIS, and most other platforms and common browsers.

Learners record their names Learners can voice-record their names or use audio databases for pronunciation. This allows instructors and peers to hear and see phonetic spellings of names.

Recordings are available across platforms Audio recordings of name pronunciations are available in many different areas across your platforms in areas like learner profiles and course rosters.

Examples of when name pronunciation AI can be used: • • • • •

Online class discussions Virtual welcome experiences Online program information sessions Tutoring sessions Student support

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Accent Recognition AI “Sorry, I didn’t understand that.”

You’ve probably heard this or something similar from voice assistants, even if you’re a native English speaker.

AI voice assistants are pretty accurate overall, though (between 85-95% accurate), depending on which one you’re using.

95% accuracy is impressive, right?

Sure, but that still means every 20th word is wrong, which is the exact length of the sentence you’re reading.

But the accuracy of these tools decreases if you have an accent, even if you’re a native English speaker with a regional accent.

A study found that Americans with Southern and Western accents were more accurately understood by voice assistants compared to people with Eastern and Midwest accents.

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But what about people in America with non-native accents?

The study showed a 30% higher rate of inaccuracies for individuals with non-native accents, especially those with Spanish and Chinese accents. This is particularly because Spanish and Chinese are the most common non-English languages in the U.S., per the Census Bureau data.

Imagine how frustrating this experience is for individuals for whom English is not their first language, and voice-related AI tools misunderstood a third of what was said.

Now, imagine if English wasn’t the person’s first language and they rely on voicerelated AI because they have a disability that impacts their ability to type.

Did you know?

22% (69.2 million people) of the U.S. population speak languages other than English.

Source: U.S. Census Bureau

The good news is that accent recognition AI tools are available, specifically designed to recognize and better understand accents.

If these AI tools are integrated with users’ devices, it could improve the operability of voice-controlled technologies and generate more accurate live captioning and transcriptions. These tools analyze and interpret speech patterns, intonations, and pronunciations specific to different accents.

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Localization AI Even when people speak the same language, where they’re from can affect how well they are understood by others and by AI. For instance, there are over 160 English dialects around the world, including American English, British English, and South African English, among others.

Typically, English speakers can understand one another despite their dialects, yet there are noticeable differences in pronunciation, grammar, and spelling.

Here’s how dialect can change the meaning of English words American English

British English

Chips

Football So, if a U.S.-based company sells “chips” in the U.K., their sales may struggle because people are confused and disappointed they didn’t get what they expected.

Similarly, learning content can be confusing if it isn’t localized. However, localization AI can automatically localize (or localising for the Brits) content for specific dialects and provide important context based on cultures and preferences.

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Remove the “what ifs” from online testing Test-takers face many “what ifs” when it comes to online testing. What if I need extra time? What if my internet isn’t fast enough? What if I need to use an assistive device?

And online proctoring can create more questions. What if my webcam is broken? What if my roommate makes noise during the test?

While online proctoring is usually just seen as a way to prevent cheating, the right type of proctoring—a blend of AI and human proctors—can offer answers and solutions to the questions and challenges that come with online testing.

What if my internet isn’t fast enough?

Honorlock won’t “boot out” test-takers if their internet speed isn’t fast enough or becomes spotty. Instead, our AI captures still images to compensate for slower networks, allowing test-takers to complete their exams.

What if my roommate makes noise during the test?

Honorlock’s AI listens for specific keywords that may indicate cheating, like “Hey Siri,” instead of flagging a noisy roommate or even if the test taker talks aloud to themselves as they work through a problem.

What if my camera or microphone is broken?

Test administrators can provide proctor instructions that allow the test taker to take the proctored test without a camera and/or microphone.

Proctor instructions can also be given to accommodate specific test taker needs, such as bathroom breaks, use of assistive devices, and more.

Learn more about Honorlock’s approach to online proctoring.

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Part 2:

AI to Develop Equitable Admissions All institutions have some form of admissions policies and procedures. Some are primarily to keep processes efficient, but some have undergone a complete overhaul— from people to technologies—to ensure that every stage of the admissions process is equitable for everyone.

But biases still exist, and they always will.

Whether intentional or not, they’re a part of human nature. Biases don’t mean you’re a bad person. In fact, not all biases are bad— like believing that all younger people are tech-savvy. However, good-natured biases can quickly turn into stereotyping and discrimination, like not hiring older people because they aren’t tech-savvy.

It isn’t realistic to completely remove biases, but they can be acknowledged and addressed. While the AI you’ll learn about in this section can help build more equitable admissions, it’s important to remember that biases can exist in AI too. © Honorlock 2024

“If you are human, you are biased.”

- Howard J. Ross, Author and Social Justice Advocate

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How can AI be biased?

Because AI reflects the biases of the people using it, the data it’s trained on, and the ways it’s used. For example, if AI is trained on biased data, such as data with underrepresented groups, there’s a ripple effect that can impact algorithms, outputs, and future models. The good news is that, similar to human biases, AI biases can also be recognized and addressed to help reduce them.

If you were tasked with selecting learners to admit into your program or institution— and let’s pretend time constraints don’t exist—would you solely rely on objective data, like standardized test scores and GPAs? Or would you also want to understand other areas of who they are?

Generally speaking, a Holistic Review, which considers personal qualities like personality, character, and life experiences, would be a wise choice. While personal qualities are more subjective than test scores, research shows that they can predict success in school and life.

But a Holistic Review is usually more timeintensive than a review based on objective information, like test scores and GPAs. In a perfect world, each institution would have a fleet of admissions personnel, with every application, personal statement, letter of intent, and resume reviewed by multiple people before making a decision.

But that isn’t the case for just about every institution.

70%

of admissions officers believe personal qualities are an important factor when selecting applicants NACAC Survey: Character and the College Admission Process. 2020.

However, large language models (LLM) could help.

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Large Language Model (LLM) Large Language Models (LLMs) understand, interpret, and generate human language by analyzing and learning from extensive datasets, enabling them to measure personal qualities in college admissions essays both equitably and accurately.

Research published in October 2023 by the University of Pennsylvania indicates that certain LLMs, if trained properly and thoroughly, can review applicants’ essays for personal qualities that predict college graduation on par with human admissions staff.

The researchers and their team analyzed over 300,000 college essays and scored them on the absence or presence of seven traits: prosocial purpose (helping others), leadership, learning, goal pursuit, intrinsic motivation, teamwork, and perseverance.

Then they trained an LLM, RoBERTa, to recognize and evaluate similar qualities and characteristics in essay submissions without showing biases toward race, gender, or socioeconomic status.

RoBERTa was used because it excels at understanding the context and meaning of language, which makes it an effective tool for understanding emotions, text classification, and translations.

Source: Benjamin Lira et al., Using artificial intelligence to assess personal qualities in college admissions.Sci. Adv.9, eadg9405(2023). DOI:10.1126/sciadv.adg9405

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RoBERTa vs. GPT? Both are LLMs that share the same architecture but excel in specific tasks, similar to cars with the same frame but different tires and suspension systems for certain terrains.

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RoBERTa drives better in the city (understanding language nuances, emotions, and context), but it can still make it on certain off-road trails (creating content).

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GPT can navigate some city streets, but it’s best for driving on any off-road trails.

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Research results & takeaways: •

RoBERTa recognized personal qualities without bias It recognized qualities like teamwork and intrinsic motivation in applicants from diverse backgrounds, without showing bias towards race, gender, or socioeconomic status.

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RoBERTa’s predictions were accurate Its predictions of the likelihood of students graduating were slightly more accurate than humans, but not by much.

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The researchers recommend using AI with optimism and caution

RoBERTa was effective, but AI isn’t perfect (and neither are humans).

The researchers offered this advice: “An AI approach to measuring personal qualities warrants both optimism and caution… We recommend AI be used to augment, not replace, human judgment. No algorithm can decide what the goals of a university’s admissions process should be or what personal qualities matter most for that community.”

While this research has nuances, like the length of the essays (only about 150 words each), it demonstrates that admissions teams can potentially use specific LLMs as a supplemental tool to review written submissions equitably and accurately during the admissions process.

Source: Benjamin Lira et al., Using artificial intelligence to assess personal qualities in college admissions.Sci. Adv.9, eadg9405(2023). DOI:10.1126/sciadv.adg9405

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Chatbot prompts to review admissions essays Chatbots can review written submissions for inclusive language, grammar, punctuation, syntax, and even plagiarism. You can customize the prompts in this section to get responses in specific formats, such as bulleted lists of errors or short explanations. For example: “Review this paragraph and provide a bulleted list of any slang or idioms that may be culturally insensitive.”

Prompts to help assess the use of inclusive language •

Check for the respectful and accurate use of terms related to individuals’ identities, such as disability, race, ethnicity, sexual orientation, sex, and gender identity.

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Identify any slang or idioms that may be culturally insensitive.

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Review the essay for any language that could be perceived as discriminatory or biased.

Prompts to review grammar, punctuation, and syntax • Identify and list any grammatical and punctuational errors in this essay.

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Analyze this essay for syntax issues.

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Review the essay for subject-verb agreement.

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Assess the use of tense throughout the essay and list inconsistencies.

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Prompts to detect plagiarism Google Bard is better for plagiarism detection because it has live access to the internet, unlike ChatGPT. •

Highlight any text that matches text from other published sources.

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Identify any text that closely paraphrases text from the original source.

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Check this essay for proper citations and attribution of quotes.

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Automated interview platforms AI-powered interview platforms can standardize admissions interviews with structured interviews that remove common challenges like travel costs and exam scheduling, and they can help reduce biases.

How do automated interviews work?

In general, the interviewee responds to pre-recorded questions, and their responses are recorded and transcribed for easy review.

Is an automated interview—a candidate talking to their screen—a good idea? It depends on who you ask, but there are a few key benefits and some drawbacks:

Did you know?

Some automated interview platforms use AI chatbots that listen to responses and ask follow-up questions, similar to an in-person interview.

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Benefits of automated interviews: •

Standardizes interviews: Automated platforms can ask the same set of questions, which helps level the playing field and ensures that there are no “curve balls” for some.

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Reduces biases: Interview responses can be transcribed and anonymized to remove biases such as accents (which decrease levels of trust), sex or gender, etc. Reading a transcript of responses can also help remove distractions, like nervous body language.

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Increases flexibility: Whether it’s school, work, or spending time with family, people have busy lives. Aside from not having to drive to a physical location for the interview, automated platforms offer the ability to complete the interview when they’re ready. The reviewer also has the flexibility to watch the interview recording and listen to specific responses again to better understand the message.

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Fewer distractions: The reviewer can read a transcript of the interview. Even though AI transcription isn’t perfect, it can help remove distractions like nervous body language and tone of voice.

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Less pressure on the candidate: Interviews can be nerve-wracking for many, but automated interviews could help reduce some candidates’ anxiety because they’re likely in a familiar setting and they’re only talking to their computer.

Drawbacks of automated interviews: •

No personal connection: While live interviews make some people nervous, others thrive on the chance to build personal connections. Automated interviews don’t provide the two-way communication needed for personal connections.

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Requires internet access and specific hardware and software: What if a candidate doesn’t have a device with a working camera or microphone? This situation presents a significant barrier if they can’t travel to a location, such as a public library, that offers the necessary requirements.

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Questions can still have biases: Questions can still contain biases: AI platforms ask the questions you provide, which may unintentionally include biases.

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Part 2:

How to Build Accessible Online Courses “The one argument for accessibility that doesn’t get made nearly often enough is how extraordinarily better it makes some people’s lives. How many opportunities do we have to dramatically improve people’s lives just by doing our job a little better?”

- Steve Krug, Author and User Experience Expert

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Accessibility is a journey When you’re building an online course, you’ll never say, “Finally, I’ve built the perfect online course that’s 100% accessible.”

That’s because accessibility in online learning is an ongoing journey. From software and compliance standards to student feedback and everything in between, accessibility and is constantly changing.

While this journey is a continuous effort with many considerations, it offers plenty of checkpoints and pit stops along the way that provide opportunities to learn and improve through trial and error. Embrace the wins and losses and use them to create a better learning experience for every single student.

This section will show you: How to build accessible online courses • Real examples and screenshots of what to do vs what not to do • A summary of POUR principles Important web accessibility compliance standards to follow • Summaries of the three main compliance standards • Financial consequences for noncompliance Assistive technology’s role in online courses • Common assistive technologies and how they work • Assistive technology vs accessible technology: what’s the difference?

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What does web accessibility mean?

It means using software, tools, technologies, and best practices so that each learner has equitable access and ability to understand, engage, and interact with all elements in the course.

Key compliance standards & guidelines: Web Content Accessibility Guidelines (WCAG) Creates shared accessibility standards for all web content like text, design, multimedia, and structure.

American Disability Act (ADA) Prevents discrimination against people with disabilities by ADA requiring all electronic information, such as websites and multimedia, to be accessible for people with disabilities.

Section 508 Federal requirement to create and maintain accessible information and communications technology for people who have disabilities. This includes hardware and software, operating systems, multimedia, and more.

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?

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What if institutions aren’t compliant?

Aside from putting the institution’s reputation at risk and creating a bad user experience, not meeting web accessibility compliance standards is a huge financial risk.

By law, universities and colleges that receive federal financial aid are required to meet web accessibility compliance standards or provide reasonable accommodations.

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How to build accessible online courses Follow POUR principles

POUR breaks web accessibility down to four guiding principles: perceivable, operable, usable, and robust.

Perceivable

Operable

Understandable

Robust

Learners can identify content & elements using their senses

Interactive elements of the user interface & navigation are operable

Learners can comprehend the content & learn

Content is usable across most devices & assistive technology

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Make your content easy to read Color contrast

Use appropriate contrast ratios to ensure that all text color is easily visible against the background color. Keep in mind that different standards have different ratios and that font size can impact contrast pass or fail.

WCAG contrast ratios

WCAG AA: at least 4.5:1 (normal text) & 3:1 (large text) WCAG AAA: at least 7:1 (normal text) & 4.5:1 (large text) Normal text = 12-14 px font Large text = 18 px font & above The example below shows the fine line between color contrast that’s accessible for all standards compared to others that only pass for larger text.

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Font types & sizes Use Sans Serif fonts because they’re easier to read compared to Serif fonts. Serif fonts have small lines at the ends of the letter (circled in the example below) that can make it more difficult to read.

Common Serif fonts:

Common Sans serif fonts:

• Times New Roman

• Arial

• Garamond

• Helvetica

• Georgia

• Calibri

Double check font sizes depending on the type Some fonts are inherently larger than others even though they may technically have the same size font point.

This sentence uses an 18pt font.

This sentence uses an 18pt font.

Even though both sentences above use Sans Serif fonts (the first is Arial, the second is Calibri) that are the same font point, they’re significantly different sizes overall.

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Structure & organize your course content Use actual headings, not just bolded font

Headings (H1, H2, H3, etc.) should clearly define the intent of the elements of your content and help screen readers understand the structure and hierarchy.

Headings should be in order Do: H1, H2, H3

Do not: H1, H3, H2

Create numbered or bulleted lists instead of hyphens Numbered (ordered) or bulleted (unordered) lists help assistive technologies, like screen readers, understand the items on the list. Hyphens don’t help assistive technology understand the items on the list.

Do:

Do not:

Topic

Topic

• Subtopic

- Subtopic

• Subtopic

- Subtopic

• Subtopic

- Subtopic

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Use a consistent course structure Using a consistent course structure helps students navigate and better understand course content because it’s predictable and easier to follow.

The “Do” layout example makes the course content easier to follow by clearly breaking out Readings and Assignments, providing further context of what the course elements are (e.g., PDF or website), and what action is needed for each assignment (e.g., submit Excel Document).

Do:

Do not:

Use descriptive hyperlink text Descriptive link text is important for people with visual impairment because it gives context and purpose. If the person is using a screen reader and the entire page says, “Click here” and “Learn more” it’s difficult to find what they need.

Hyperlink text tips: • Changing the color of the hyperlink text isn’t enough • Hyperlink text should provide the context and purpose of the link • Do: Read Article 1 for Week 2 • Do not: Read more

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Create text alternatives for multimedia All video, audio, images, and any other multimedia content should have text alternatives (captioning & subtitles, transcripts, and descriptions) to help students perceive and understand the content.

Video captions & subtitles These terms are often used interchangeably, but it’s important to understand the differences.

Subtitles = what’s said Captions = what’s said + non-speech elements (a dog barking) Captioning tip: only provide enough detail to help students understand what’s happening. Too many details, like in this example, can be distracting without providing any beneficial information.

Do this:

Don’t do this:

Don’t use video content that flashes or blinks!

Content that flashes or blinks can cause seizures. WCAG recommends that if content must flash or blink, students should be able to disable it and there shouldn’t be more than three flashes/blinks in one second.

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Audio descriptions & transcripts Audio descriptions and transcripts are meant to provide important visual aspects of the video for those who are unable to see.

Image text descriptions Image text descriptions provide the context and purpose of the image. This text is used in place of the image and improves the accessibility of the page for users with visual impairments.

Tips for writing image text descriptions:

Tip! Just like video captions, audio descriptions should be concise so that the listener isn’t overwhelmed with too much information.

• Accurately describe the image in 125 words or less (avoid using one word only) • Don’t use “image of” or “picture of” or similar wording • Read it out loud to ensure that it makes sense

Dog swimming Image of a dog swimming underwater Golden retriever swimming underwater in a pool © Honorlock 2024

Graph of streaming networks Line graph Netflix vs. Prime Line graph showing an upward trend in Prime customers from 2020 through 39 2024, and a downward trend for Netflix.

AI for DEI in Online Education

Provide accommodations Students may need a variety of accommodations, such as: • Extended time limits and due dates • Bathroom breaks • Alternative ways to answer questions • Allowing other people in the room • Permitting the use of assistive technologies, like screen readers and talk-to-text dictation software

Incorporate diverse third-party content Find different types of content to use in your course from a variety of sources. This helps students understand different perspectives, ideas, and beliefs about a subject.

Types Use different types of content, like research studies, news articles, magazines, case studies, books, videos, and more.

Sources Find content from at least two sources from different backgrounds, such as those from other genders, races, ethnicities, and religions.

Ask for feedback & more information Use anonymous surveys throughout the course to gather student feedback and information about their experience, accessibility needs, what they enjoyed, and what can be improved.

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Create a central location for resources Create a central location, whether it’s in the syllabus or the course homepage, that gives students a comprehensive list of help resources, such as: • Campus writing center and tutoring services information • Links to the institution’s online library and open educational resources (OER) • Technical support access • Student study groups • Contact information for accessibility-related departments and personnel You should also provide specific hardware and software used in the course, such as the LMS and how to use it, how online proctoring software works, and minimum system requirements such as the appropriate devices, operating system, browser, and internet speed.

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Assistive technologies Assistive technologies are software and hardware that helps students understand, engage, and interact with online course content.

Common assistive technologies include: • Screenreaders • Assistive keyboards • Dictation software • Braille displays • Screen magnifiers

Assistive technology vs accessible technology The terms are used interchangeably, but they’re different.

Assistive technology

Designed to help students perform specific tasks.

• e.g., dictation software turns spoken words into text

Accessible technology

Designed to meet a broad range of needs.

• e.g., the LMS provides customizable colors and fonts and online proctoring software that integrates with assistive technology

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AI for DEI in Online Education

DEI is an ongoing journey AI helps clear paths. Educators lead the way.

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