Comment on Scott and Greg’s Discussion: Where Is Technology Taking Mentoring?

Scott and Greg are having an interesting discussion in “Scaling Up Mentoring” (in Scott’s blog, Online Learning, 3.14.12). Scott asks, “Can we build a digital mentor?… This is technology that can intelligently design content based on world wide information so that we can do something useful:  learn.  The question is how?” He continues, “In a sea of world-wide information, we need a focus.  We need a context for all the information out there so that we can determine what is relevant, reflect on it, and work it into our schemas.”

Scott lists some specs for “a digital mentor”:  “At the outset, a digital mentor would need to 1) know what you are trying to do (or learn), 2) be able to filter world-wide information in order to draw connections between relevant information…  without our help, of course, and 3) present that information to us in a meaningful way.”

I think the specs are dead on. Despite all the innovation, the web is still relatively dumb. It can do what it’s programed or directed to do, but that’s it. But intelligence is inevitable. For now, all the smarts are on the “other” side — on the side of the megasites that want to sell us something or web media that serve the megasites by collecting our browsing profiles and matching them to products and services. It’s the intelligence that greases the wheel of profit.

In time, we’ll have e-ntelligence on our side, too. Our browsers will learn from us, individually, tapping into cloud-based supercomputing for the analytics engines with the capacity to do this.

In fact, some of our personal e-devices and apps are beginning to show some smarts. Our browsers, for instance, can facilitate our searches by storing a “history” of our browsing. Primitive, but it’s a form of learning.

In the not-so-distant-future, though, I can imagine computers that will constantly learn from us by (1) “sieving” or gathering everything we read, write, view, hear, and say, (2) “gisting” or getting a gist of what we’re interested in or where we might be headed even before we’re conscious of  it, (3) shifting and pointing in the general direction as data is being processed,  (4) following our lead as we pick, ignore, dig into sources and using this data to inform and refine the ongoing search, (5) multinavigating as we leave the thread of our search for other threads — and returning to earlier threads or completely different ones on our cue.

This e-ntelligent learning assistant, or ela, will work 24-7 so we can leave and return anytime and continue or move on to new ideas. She will learn our habits and styles and adapt. The longer we work with her, the better she gets to know us and becomes, in time, an extension of our selves online.

Her presence will be most evident when we write or speak. She’ll be literally reading our mind as it has been and is being continually reconstructed in her digital memory. As we begin to type the title for a source, she’ll complete it, based on our past e-actions, in reverse chronological order. And from the context, she’ll determine whether we want it hyperlinked or not. This also means that grammatical errors, spelling, word choice, documentation, organization, sentence patterns — all the elements of writing — will gradually come under her radar and individualized services.

When we’re developing an idea, she’ll be simultaneously scanning the latest epublications on the web for appropriate resources and sharing them with us. She’ll select only the ones that will serve our specific needs for a specific sentence. paragraph, or article. Furthermore, she’ll select relevant quotes or facts for us so we don’t have to read the article. If we want to include it, a barely noticeable nod will suffice. Yes, she can see us via the cam on our screens. In time, she’ll learn to interpret our gestures and facial expressions. After we’ve worked with her a while, we’ll simply pose a question or present an idea and ask her to write a draft for a particular purpose or audience. And she’ll know us well enough to write it in a style that’s appropriate for us.

The implications for learners will be immense. Ela will be a constant behind-the-scenes online companion, gauging where the student is, where she needs to go, and the size and direction of the incremental steps she’ll need to take, based on formative tests or quizzes or simply ela’s observations. The student’s performance, interests, and learning goals will drive ela. Ela will also be able to create on-demand reports on-the-fly for “teachers” and parents.

Who knows. In time we’ll ask ela to represent us at virtual meetings, and she’ll perform as we might have had we been there. In fact, we could ask her to teach our classes, too, while we go surfing. She’ll be an avatar, of course, appearing in students’ virtual learning environments. At some point, a meeting or class could be made up entirely of avatars — avatar teachers, colleagues, students.

OK, I’m getting carried away. It’s already past 1 pm and I’ve missed another live session. But this field trip into Scott’s blog is one I couldn’t resist.

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