Overcoming Age Bias in Technology Instruction

While I’m updating this website, I want to share this article to encourage anyone over age 45 who may be considering a second career in librarianship or information technology. Of course, you can do it. You may even change the way society views “older adults” and technology. You may even become the next Marian Croak.

The following is an excerpt from a research proposal that I wrote in 2020 for an Information Communication and Technology course. I made a few minor revisions this weekend, but the essence of the paper is the same. The proposed study certainly isn’t a new idea. You are welcome to download the paper and read the abbreviated literature review.


It is a widely known but not often discussed fact that age bias exists in the tech industry. This paper outlines how ageism in tech industry hiring practices creates a bias in the design of tech products, which encourages bias in product marketing, and, ultimately, affects the training provided for new technologies and the adoption of new technology by older adults.

As discussed during a course lecture,  while bias is not always intentional, it can create a technological deck stacked in favor of specific social interests (Cooper, 2020). 

As Winner  (Winner, 1980, p. 127) states, “technologies” are important in everyday life and contain possibilities for many different ways of ordering human activity. He (Winner, 1980, p. 127) states, “consciously or not, deliberately or inadvertently, societies choose structures for technologies that influence how people are going to work, communicate, travel, consume, and so forth over a very long time.

For example, technology contributes to the preferred mode of communication in an organization’s workplace culture. Should you send an email, use chat, or make a phone call? There are no longer public payphones on city streets because everyone is assumed to have a mobile. Public transit drivers, who frequently change routes, can no longer be expected to provide passengers with directions. There is an assumption that most individuals can use apps like Google Maps to navigate. And during the current global pandemic, technology has allowed millions of individuals to telework, shop online, participate in religious services, or connect with family and friends, while millions of others could not.

Winner (1980, p. 127) states that when society chooses structure for technology, groups are differently situated and possess unequal degrees of power and levels of awareness.

Dishman cites Karissa Thacker’s point that “ageism is rampant,” and the narrative that most older adults are more interested in fishing than using technology has been ingrained into our collective consciousness (Dishman et al., 2015).

  • What is the origin of these misperceptions about older adults and technology use?
  • Does the bias in technology design affect how older adults adapt to new products?
  • Is there bias in technology instruction? If so, how can it be addressed?

This paper examines those questions and proposes a research study to determine if a public library program teaching self-directed, online learning skills will benefit older adults.

The Facts about Older Adults and Technology Use

A 2017 Pew Research study found that as the U.S. population aged, the use of ICTs (smartphones, internet, and social media) by older adults’ increased. For example, smartphone ownership by older adults aged 65 and older increased from 18% in 2013 to 42% in 2017, and nearly half had home broadband (Pew Research Center, 2017). While this indicates that the digital divide between older adults and their younger counterparts is narrowing, one-third of older adults said that they have never used the internet, and smartphone ownership in this group was 42 percentage points lower than that for adults ages 18 to 64 (Pew Research Center, 2017).

As evidenced in studies of all age groups, there were substantial differences in technology use by older adults based on education and socio-economic variables. (Pew Research Center, 2017). The study found that:

  • 87% of seniors with an annual household income of $75,000 or more a year said they had home broadband, compared to 27% of seniors whose annual household income was below $30,000.
  • Technology use was higher among college graduates than seniors with lower levels of formal education
  • smartphone ownership among seniors with household incomes of $75,000 or more increased by 39 percentage points since 2013 – 15 points higher than the growth reported among seniors overall.
  •  34% of older internet users said they had little to no confidence in using electronic devices to perform online tasks. And 48% of seniors said they needed assistance configuring and using a new device. (Pew Research Center, 2017)

Pictures seldom seen

Overall, the Pew study found that 58% of adults age 65 and older have a positive view of technology,  three-quarters of internet-using seniors say they go online daily, and nearly one in ten go online almost constantly  (Pew Research Center, 2017). However, this is not the image of older adults that is widely portrayed by society. 

More than a third of the United States population is older than 50, but the group turns up in only 15 percent of media images, according to research from AARP (Hsu, 2019). According to the Bureau of Labor Statistics, over 53 million people older than 50 are employed, making up a third of the American labor force. But only 13 percent of the images reviewed by AARP showed older people working. (Hsu, 2019). Yet less than 5 percent of the images showed older generations handling technology, even though the Pew Research Center has found that 69 percent of people between 55 and 73 own smartphones. More than a third of the images analyzed by AARP portrayed younger people with technology. (Hsu, 2019)

Problem Statement

As Orlov (2019) reported, age bias permeated advertising and technology design.

As Dishman (Dishman et al., 2015) pointed out in a 2015 article for Fast Company, much of the age bias in tech stems from  the infamous 2007 comment by Mark Zuckerberg,

“I want to stress the importance of being young and technical. Young people are just smarter. Why are most chess masters under 30? I don’t know. Young people just have simpler lives. We may not own a car. We may not have family. Simplicity in life allows you to focus on what’s important.”   (Kane, n.d.)

Recruiters embraced the Zuckerberg philosophy so much so that a Santa Clara-based firm had a message on their Careers page that read,  “We Want People Who Have Their Best Work Ahead of Them, Not Behind Them” (Dishman et al., 2015).   Orlov (Orlov, 2019) explains that the average Silicon Valley tech company employee is a millennial (age 20-33), not a boomer between the ages of 52-70, so there is little to no design input from older adults into the smartphones, tablets, and apps that at least 69% of them use.  This creates an implicit bias in the technology design. 

Friedman (Friedman & Nissenbaum, 1996, p. 334) explains preexisting bias has its roots in social institutions, practices, and attitudes and can enter a system either through the explicit and conscious efforts of individuals or institutions or implicitly and unconsciously, even despite the best intentions. For example, individual bias originates from individuals who have significant input into the system’s design. In contrast, societal bias stems from society at large  (e.g., gender biases present in the larger society that lead to the development of educational software that overall appeals more to boys than girls) (1996, p. 334). There is also an emergent bias that develops after a new tech product is rolled out to the public due to changing societal knowledge, population, or cultural values (1996, p. 335). As Friedman (1996, p. 335) notes, user interfaces, such as web pages, application toolbars, and smartphone screens, are prone to emergent bias because they are designed based on perceptions about the intended end-users. Freidman defines the types of bias that frequently impact older adults as:

  • Mismatch between Users and System Design – Bias that originates when the population using the system differs significantly from those assumed as users in the design.
  • Different Expertise – Bias that originates when the system is used by a population with a different knowledge base from that of those assumed in the design.
  • Different Values – Bias that originates when the system is used by a population with different values than those assumed in the design. (1996, p. 335)

For the first time, five generations, Traditionalists (born before 1946), Baby Boomers (1946-64, Gen X (1965-80), Millennials (1981-96), Gen Zs (born after 1997), could be co-workers, and this mix of digital natives and digital immigrants has the potential to become what Dishman termed  “hotbeds of generational bias” (Dishman et al., 2015)

“Digital Natives” are individuals born after 1980 who have been exposed to technology since childhood and are the “native speakers” of the digital language (Prensky, 2001). On the other hand, “Digital Immigrants” are individuals born before 1980 who are learning the language and culture of the digital age. Prensky (Prensky, 2001, p. 2) found that Digital Immigrants speak the language of “Digital Natives” but with “an accent and are storing the new language in a different part of the brain. As a result, Digital Natives process information quicker, enjoy multi-tasking and gaming more, and have little patience for the processing style of Digital Immigrants.

Prensky provides a persuasive argument for Digital Immigrant instructors to learn to communicate in their students’ language and style (2001, p. 4). However, it is also crucial for Digital Natives to modify their language and address their biases towards older adults when they are delivering technical instructions to Digital Immigrants.

Cut (Čut, 2017) observed that Digital Immigrants fall into three categories: 

  • Avoiders: they prefer a relatively minimal technology or technology-free lifestyle. They do not have email accounts and/or smartphones and tend to have deadlines. Social media is too much for them, and they do not see the value in these activities.
  • Reluctant adopters accept technology and are trying to engage with it but find it unintuitive and hard to use. For example, they have a cell phone but do not use texting, occasionally they use Google but do not have a Facebook account, but they check their emails and use online banking.
  • Enthusiastic adopters: they are digital immigrants who have the potential to keep up with natives. They embrace technology and may be high-tech executives, programmers, and businesspeople. This group sees the value of technology; they use Facebook and check emails regularly, which makes them excited. If they are doing business, they have a website. 


The overarching research question is:  Can a program designed to teach online learning skills to older adults (age 55+) increase that group’s use of self-directed technology training resources, such as TechBoomers and GCFLearnFree, available through public libraries?

The research project would use instructional design theory to create a course for “Avoiders” and “Reluctant Adopters” that teaches basic website navigation, information seeking, and self-directed learning. The instruction would be delivered in a small group setting in a public library. Participants could bring their own devices or use the library’s public computers, loanable laptops, or tablets. After the instruction, the participants would be surveyed to answer the following questions: 

  • Was the course effective in improving the participants’ general awareness of the available resources for self-directed technology instruction?
  • Did the course improve the participants’ ability to search for information on the websites?
  • Were the participants able to follow the instructions in a technical training tutorial and successfully complete the tasks?
  • Were the participants able to complete a tech training outside of the library setting training setting, either at home or without assistance using a library computer?

Feel free to download this paper to read more.