Tutorbots are teaching chatbots. They realise the promise of a more Socratic approach to online learning, as they enable dialogue between teacher and learner.
We have seen how online behaviour has moved from flat page-turning (websites) to posting (Facebook, Twitter) to messaging (Txting, Messenger). We have see how the web become more natural and human. As interfaces (using AI) have become more frictionless and invisible, conforming to our natural form of communication (dialogue), through text or speech. The web has become more human.
Learning takes effort. So much teaching ignores this (lecturing, long reading lists, talking at people). Personalised dialogue reframes learning as an exploratory, yet still structured process where the teacher guides and the learner has to make the effort. Taking the friction and cognitive load of the interface out of the equation, means the teacher and learner can focus on the task and effort needed to acquire knowledge and skills. This is the promise of tutorbots. But the process of adoption will be gradual.
I’ve been working on chatbots (tutorbots) for some time with AI programmes and it’s like being on the front edge of a wave.... not sure if it will grow like a rising swell on the ocean or crash on to the shore. Yet it is clear that this is a direction in which online learning will go. Tutorbots are different from chatbots in terms of the goals, which are explicitly ‘learning’ goals. They retain the qualities of a chatbot, flowing dialogue, tone of voice, exchange and human (like) but focus on the teaching of knowledge and skills.
The advantages are clear and evidence has emerged of students liking the bots. It means they can ask questions that they would not ask face to face with an academic, for fear of embarrassment. This may seem odd but there’s a real virtue in having a teacher or faculty-free channel for low level support and teaching. Introverted students, whom have problems wit social interaction, also like this approach. The sheer speed of response also matters. In one case they had to build in a delay, as it can respond quicker than a human can type. Compare that to the hours, days, weeks it takes a human tutor to respond. It is clear that this is desirable in terms of research into one to one learning and the research from Nass and Reeves at Stanford confirmed that this transfer of human qualities to a bot is normal.
But what can they teach and how?
1. Teaching support
I’ve written extensively on the now famous Georgia Tech example of a tutorbot teaching assistant, where they swapped out one of their teaching assistants with a chatbot and none of the students noticed. In fact they though it was worthy of a teaching award. They have gone further with more bots, some far more social. Who wouldn’t want the basic administration tasks in teaching taken out and automated, so that teachers and academics could focus on real teaching? This is now possible. All of those queries about who, what, why, where and when can be answered quickly (immediately), consistently and clearly to all students on a course, 24/7.
2. Student engagement
A tutorbot (Differ) is already being used in Norway to encourage student engagement. It engages the student in conversation, responds to standard inquiries but also nudges and prompts for assignments and action. This has real promise. We know that messaging and dialogue has become the new norm for young learners, who get a little exasperated with reams of flat content or ‘social’ systems that are largely a poor-man’s version of Facebook or twitter. This is short, snappy and in line with their everyday online habits.
3. Teaching knowledge
Tutorbots, that take a specific domain, can be trained or simply work with unstructured data to teach knowledge. This is the basic workaday stuff that many teachers don’t like. We have been using AI to create content quickly and at low cost, for all sorts of areas in medicine, general healthcare, IT, geography and for skills-based training using WildFire. Taking any one of these knowledge-sets, allows us to create a bot that re-presents that knowledge as semi-structured, personalsed dialogue. We know the answers, and recreate the questions with algorithmic tutor-behaviours. The tutorbot can be a simple teacher or assessor. On the other hand it can be a more sophisticated teacher of that knowledge, sensitive to the needs of that individual learner.
4. Tutor feedback
Feedback, as explained by theorists such as Black andWilliam, is the key to personalised learning. Being sensitive to what that individual learners already know, are unsure about or still need to know, is a key skill of a good teacher. Unfortunately few teachers can do this effectively, as a class of 30 plus or course with perhaps hundreds of students, means it is impractical. Tutorbots specialise in specific feedback, trying to educate everyone uniquely. Dialogue is personal.
5. Scenario-based learning
Beyond knowledge, we have the teaching and learning of more sophisticated scenarios, where knowledge can be applied. This is often absent in education, where almost all the effort is put into knowledge acquisition. It is easy to see why – it’s hard and time consuming. Tutorbots can pose problems, prompt through a process, provide feedback and assess effort. Bots can ask for evidence, even asses that evidence.
6. Critical thinking
As the dialogue gets better, drawing not only on a solid knowledge-base, good learner engagement through dialogue, focussed and detailed feedback but also critical thought in terms of opening up perspectives, encouraging questioning of assumptions, veracity of sources and other aspects of perspectival thought, so critical thinking will also be possible. Tutorbots will have all the advantages of sound knowledge to draw upon, with the additional advantage of encouraging critical thought in learners. They will be able to analyse text to expose factual, structural or logical weaknesses. The absence of critical thought will be identified as well as suggestions for improving this skill by prompting further research ideas, sound sources and other avenues of thought.
7. General teacher
The holy grail in AI is to find generic algorithms that can be used (especially in machine learning) to solve a range of different problems across a number of different domains. This is starting to happen with deep learning (machine learning). The tutorbot will not just be able to tutor in one subject alone, but be a cross-curricular teacher, especially at the higher levels of learning where cross pollination is often fruitful. It will cross-departmental, cross-subject and cross-cultural, to produce teaching and learning that will be free from the tyranny of the institution, department, subject or culture in which it is bound.
As a tutorbot does not have the limitations of a human, in terms of forgetting, recall, cognitive bias, cognitive overload, getting ill, sleeping 8 hours a day, retiring and dying - once on the way to acquiring knowledge and teaching skills, it will only get better and better. The more students that use its service the better it gets, not only on what it teaches but how it teaches. Courses will be fine-tuned to eliminate weaknesses, and finesse themselves to produce better outcomes
We have to be careful about overreach here. These are not easy to build, as tutorbots that do not have to be ‘trained (in AI-speak ‘unsupervised’) are very difficult to build. On the other hand trained bots, with good data sets (in AI-speak ‘supervised’), in specific domains, are eminently possible – we’ve built them.
Another warning is that they are on a collision course with traditional Learning Management Systems, as they usually need a dynamic server-side infrastructure. As for SCORM – the sooner it’s binned the better.
Finally, this at last is a form of technology that teachers can appreciate, as it truly tries to improve on what they already do. It takes good teaching as it’s standard and tries to eliminate and streamline it to produce faster and better outcomes at a lower cost. They are here, more are coming, resistance is futile!