AI literacy refers to the understanding of artificial intelligence and the ability to use and interact with AI technologies effectively. It is essential in today’s world, as AI becomes increasingly integrated into various aspects of daily life. To apply Doug Belshaw’s 8 elements of digital literacies to the concept of AI literacy, we can adapt each element to focus on AI-related skills and understanding:
Cultural: Recognizing how AI technologies impact different cultures and societies, as well as understanding the ethical considerations related to AI deployment. This includes being aware of cultural biases in AI systems and acknowledging the implications of AI on the workforce and social structures.
Cognitive: Developing the mental processes involved in using AI tools, such as understanding the basic principles of AI, machine learning, and data analysis. This encompasses skills like interpreting AI-generated insights, recognizing the limitations of AI systems, and understanding the potential consequences of AI decision-making.
Constructive: Gaining the ability to create, modify, and contribute to AI technologies, including working with AI algorithms, designing AI-based solutions, and understanding the software and hardware components of AI systems.
Communicative: Effectively communicating and collaborating with others regarding AI concepts, tools, and applications. This includes being able to explain AI systems to non-experts, discussing ethical and social implications, and sharing AI-related knowledge with others.
Confident: Becoming comfortable and confident in using AI tools and navigating AI-based systems. This involves having the self-assurance to explore, learn, and adapt to new AI technologies and applications.
Creative: Leveraging AI technologies to express oneself creatively and inventively, such as using AI-generated art, music, or writing tools, and applying AI to develop innovative solutions to problems.
Critical: Critically evaluating AI technologies, algorithms, and their implications on society. This involves understanding issues like fairness, accountability, transparency, and privacy in AI systems, as well as assessing the reliability and limitations of AI-generated outputs.
Civic: Engaging with AI technologies to participate in civic activities and social and political life. This includes using AI for social good, advocating for responsible AI policies, and promoting digital equity in AI access and education.
By incorporating these adapted elements into AI literacy, individuals can develop a comprehensive understanding of AI and its impact on society, as well as the skills necessary to thrive in an AI-driven world.
That’s… not bad? I think this would actually be a decent basis to create a framework for an institution that could end up as a curriculum.
Open Badges are web-native digital credentials that allow anyone to recognise anyone else for anything. One popular approach is microcredentialing, although there is a growing movement around Open Recognition.
It’s not long since the third major version of the standard was released, with this one aligning with the W3C’s Verifiable Credential data model. If that sounds like a mouthful, and if reading specification documents makes your eyes glaze over, here’s a few highlights to explain what’s new.
1. A Farewell to Email Addresses
Although it’s technically been possible to use something other than email addresses in previous versions of the Open Badges standard, almost nobody has done so. Using an email address as an identifier can be problematic in terms of privacy, security, and long-term maintenance, as email addresses can change or become inactive over time.
Verifiable Credentials use a decentralised identifier (DID), which provides a unique, persistent, and secure way of proving who you are. This can be based on virtual wallets built into web browsers and smartphone apps, although they don’t have to be. In fact, you can generate a DID from a phone number or email address.
The DID method ensures greater privacy and security, as well as long-lasting recognition of achievements, independent of changes in the recipient’s email address. Although there may be a little bit of confusion to begin with, hopefully badge platforms will make this extremely easy to use.
2. Image-free recognition
One of the mandatory requirements of Open Badges is to use some sort of image. In fact, the metadata is hard-coded into the image as part of the ‘baking’ process. I do like a good badge image, but sometimes they can be a barrier to recognition because organisations want to ensure consistency with a house style.
With Open Badges v3.0, the alignment with the Verifiable Credentials data model means that there is no longer any requirement for an image. Verifiable Credentials are primarily focused on data and use something called JSON-LD (a standard for linked data) to describe the content. This approach means that the badge/credential is both human- and machine-readable.
While I hope it’s not the end for images in badges, I do think that it’s incredibly helpful to be able to recognise others quickly and easily.
3. Greater control
With Open Badges, the badge earner has to either share none of the details (the metadata) about their badge, or all of it. Verifiable Credentials allow for more granular control using ‘Verifiable Presentations’. This means that holders can choose what information to share and with whom, giving them greater autonomy and flexibility.
There are all kinds of things possible with this approach, including for example having an ID card in the form of a Verifiable Credential. Using the Verifiable Presentation approach, an individual could, for example, remain anonymous while still being able to prove that they are of a legal age to buy alcohol, or have the correct licence to drive a car.
In a learning context, someone could choose to create a Verifiable Presentation of several of their badges/credentials for the purposes of applying to university or for a job. Alternatively, the Verifiable Presentation could be made up of different people’s data showing the skills and achievements of a cohort. It’s very flexible.
Conclusion
As the technological landscape of learning and development continues to evolve, it’s important for educators and organisations to understand what’s possible. While Open Badges v2.1 is a great standard upon which to build, the opportunities with v3.0 using the Verifiable Credentials data model are exciting! I’m looking forward to starting to issue badges using the new approach, and sharing more information as I go.
When WAO starts working with organisations, the most important thing we have to figure out is how decisions are made. After we’ve established that, the second is the organisation figures out how best to serve their audience. The latter can be done in several ways, but there’s no substitute for talking to people!
In our experience, there’s quite a few reasons why organisations might avoid user research. Let’s have a look at a few of the most common along with some arguments against (and ways around) them.
1. Inadequate understanding of its value
The world is not slowing down, and product development and service delivery are particularly fast-paced environments. That means it’s not uncommon for managers and stakeholders to overlook the vital role that user research plays in their success. This oversight may stem from a lack of comprehension regarding the tangible benefits that user research brings to the table. As a result, decision-makers might be hesitant to allocate resources towards research initiatives.
However, by shedding light on the value of user research and illustrating its impact on the effectiveness of products and services, we can help foster a deeper appreciation among stakeholders. Quantitative data is important, but gaining qualitative data from users or your audience is vital. It’s the difference between ‘having a better value proposition’ and the realisation that your core audience doesn’t think that your product or service is actually for them.
2. Overconfidence in existing knowledge
People are promoted within organisations often because of their understanding of the sector in which they work. However, it can be dangerous to think that previous lived experience or a particular view constitutes the ‘truth’ of the situation. This can lead to thinking that user research is superfluous. Such overconfidence is usually based on anecdotal evidence, personal experiences, or preconceived notions that may not accurately reflect the broader user base.
If we acknowledge the limitations of these informal insights, then we can emphasise the importance of user research in painting a more comprehensive and diverse picture of users’ needs. By doing so, organisations can make more informed decisions and avoid the pitfalls of overconfidence, which ultimately results in more successful products and services for their users or audience.
3. Fear of negative feedback
No-one particularly likes to hear that they or their organisation are doing anything other than a good job. So it’s natural for people to be wary of negative feedback. The problem is that this apprehension can sometimes give rise to resistance towards user research, as decision-makers may be reluctant to uncover potential issues or face criticism.
There is a way of reframing this mindset by embracing the notion that constructive feedback is a valuable opportunity for growth and improvement. Looked at this way, organisations can overcome hesitations and appreciate the indispensable role that user research plays in enhancing their offerings. In the end, it’s through facing these challenges head-on that organisations can truly thrive and achieve long-term success.
4. Short-term focus
As mentioned above, we live in a fast-paced world with organisations tending to focus on short-term objectives and instant outcomes. User research, on the other hand, represents a long-term investment in product development, which might not always align with the immediate ambitions of an organisation or its decision-makers.
However, when undertaking user research for the first time, or for the first time in a while, immediately-actionable insights often are forthcoming. Coupled with the long-term value that user research brings to the table, organisations can strike a balance between short-term wins and sustainable success. In doing so, they can foster a more holistic approach to product development that not only meets immediate needs but also paves the way for a future-proof, user-centric experience.
5. Limited resources
User research is a time-consuming process. I’m well aware of this as my wife (Hannah Belshaw) is a user researcher! As such, organisations might find themselves facing constraints around budgets or staff, which can make it challenging to plan and carry out research sessions. More frequently, they may opt to prioritise other initiatives over user research.
Nevertheless, by acknowledging the long-term value that user research brings to product development and service design, organisations can make a conscious effort to allocate resources effectively. User research can be weaved into the fabric of strategic planning to ensure that their products and services continually evolve to meet the needs and expectations of their users. This ultimately drives long term success.
6. Lack of expertise
Organisations often don’t know what they don’t know about user research. Sometimes they don’t have the required in-house expertise for designing, implementing, and analysing user research sessions. This gap in their knowledge can make it difficult to derive actionable insights which means that they’re hesitant to get started in the first place.
Once this challenge has been recognised, organisations can seek to bridge that gap in their expertise, either through training existing staff, hiring new ones, or partnering with external experts. By tapping into the wealth of knowledge that user research provides, organisations they can ensure that their products and services evolve in sync with the ever-changing needs of their users.
7. Resistance to change
The idea of user research is to gain insights to improve products and services. Sometimes, these insights can lead to a call for substantial alterations which, understandably, might be met with resistance. After all, if you’ve invested considerable time and effort into the existing design, then you might not be best pleased if you’re being asked to change course. In addition, there are many people and organisations who are naturally averse to change of any sort.
However, by embracing the notion that adaptability is key to long-term success, organisations can begin to adopt a growth mindset. By navigating this resistance to change they can make use of user research insights to create better, more user-centric products and services that ultimately stand the test of time.
8. Other factors
There are many other reasons barriers to performing effective user research. For example, gaining buy-in or support from leadership can be an uphill battle, making it difficult to secure the necessary resources or cooperation. Then there are sometimes privacy and legal concerns, particularly when handling sensitive information or user data. And, of course, logistical complexities in terms of co-ordinating with research participants, internal teams, and external organisations can be off-putting.
To overcome these additional obstacles, organisations can focus on ensuring proper legal guidance when dealing with sensitive data — which is something they should be doing anyway! Effective project management should help with difficulties around logistics, and it’s always a good time to start fostering a culture of research appreciation among leadership.
Conclusion
User research is a form of superpower for organisations. Doing it effectively means the difference between designing products and services that work for your users and audience, and creating a barrier between you and them. It’s no silver bullet, but if the best time to start was several years ago, the second best time to start is today!
