In the past I’ve written about bots and the future of education. I countered this with a recent post about the future including a human touch and I think about the opportunities for bots, AI, and the user experience.
I think there are ways that we should frame this look at “bots” and extend it a bit as we think about the possible future for education. The work of making technology more human is hardly new. Voice assistants are now ubiquitous, and the kiosk bots that engage mall shoppers will soon be old news.
The name “bots” might be confusing to use as there are multiple instances of “bots” already present online. An example of this include the fleet of bots that comprise most of the traffic on Twitter.
Bots (in this instance) also may include varying levels of artificial intelligence (AI).
The 2020 Tech Trends report from Deloitte identifies this as one of the top disruptors (digital reality, cognitive technologies, & blockchain) in upcoming years. As things change in the coming decade, these newer trends may no longer be considered novel, but they’re on the cusp of becoming as familiar and significant as their predecessors.
Cognitive technologies, such as machine learning, neural networks, robotic process automation, bots, natural language processing, neural nets, and the broader domain of AI, have the potential to transform nearly every industry. These technologies personalize and contextualize the human-technology interaction, allowing businesses to provide tailored language- and image-based information and services, with minimal or no human involvement.
Machine learning bots can provide enormous opportunities to support learners in various environments. They can quickly identify the likely cause for contact from the learner, and even suggest a proactive outreach. With these changes, intuitive bots can increasingly handle learner contacts that traditionally required human agents.
I was in a recent demo from a learning management software in which they were trying to sell their suite of data analytics portals to help administrators and instructors better serve students. The main feedback from our team was that most instructors are already trying to negotiate different signals in the classroom, and this data dashboard was either too creepy, too impersonal, or just too much.
To think about bots in learning environments, we have to think about the use of contextual information the bot may have access to, but the instructor may not.
First, there is a tremendous amount of contextual information that exists within the learning environment. When you sign in, you agree to permissions with your browser, and in turn with the bot. With these permissions, it has the potential to collect a ton of contextual information. It’ll know who you are, as well as contextual information like location, peers, etc.
Second, the structure provides opportunities for lightweight interactions with very little friction.
With this new environment, think of it as a platform built on top of another platform. Think about it as the inclusion of a web browser on top of Windows, Mac OS, Linux, or your operating system. We see this happen in some apps already available in the market. Retailers are integrating AI-powered bots to personalize customer interactions while at the same time capturing valuable lead-nurturing data.
If you connect these elements together, an example is shown in the opportunity to purchase flowers.
If you want to order flowers, you need to go to the webpage, create an account, enter your credit card information, select where you’re sending it, enter billing and shipping addresses, etc.
If you do this with a bot, you’d indicate that you want to order flowers. The bot for that service would take over like a digital concierge.
The bot would already have a bunch of this contextual information. It would have billing, your current location, friends, etc. It can tell you to go to a nearby shop and look at, or order the flowers. It can handle everything for you and send flowers to your mother.
Since it has your information, and may have her address through your address book, it can handle this without you doing anything.
From a learning context, you can see a time where a student would be in the learning environment late at night trying to complete an assignment. They would have questions about some of the materials possibly not making sense. The instructor is not available for synchronous chat, and office hours are hours from now.
The bot can act as a guide on the side and assist with some resources that may help. The bot can recognize the prior achievement of the learner and adjust the level of support it provides. The bot can provide realtime assurance by walking through the assignment with the learner, and either collecting the assignment, providing feedback and a chance to resubmit, or granting an extension of the deadline if things get too pressing.
In this piece, I understand that some may not like the consideration of removing the human from learning interactions and centering machine learning interfaces.
We need to remember that friction is a relative concept. One person may view this as a handy or pleasant way of interacting. Others may view it as impersonal and not responsive. All individuals…and all learners are not the same. Some may value these interactions without the instructor, or peers seeing them struggle.
We need to ensure that this does not become a faceless interaction. As we move toward more automation and self-service, there is sometimes the belief that we can reduce all opportunities for human contact. This would be a mistake. There is a need for humans to be used in instances where a learner needs something extra or more personal. The bot can handle mundane TA-like tasks, while the human can demonstrating how well you understand the needs of the learner and offer support that is tailored to those needs.
We need to remain flexible to allow for those special moments. We sometimes have that WOW!!! moment when we get great customer service. Our favorite store knows how often we’re there, and gives us a coupon, or free product. Technology, training and company culture all work together to create space for those moments. In learning environments, we need a balance between reducing friction, while not losing academic rigor or standardized approaches. We want to differentiate learning and assessment to make the learner feel listened to and valued.
Done well, the use of bots in education offers an opportunity to free up the instructor while offering better scaffolding for learners. Educators can be freed up from the traditional frustrations of data collection, report filing,n and administrative tasks.
Technology provides the starting point, but we cannot lose high touch when we move to high tech. Culture and professional development for learners, instructors, and support staff are even more important.