Sunday, December 17, 2017

10 uses for Chatbots in learning (with examples)

As chatbots become common in other contexts, such as retail, health and finance, so they will become common in learning. Education is always somewhat behind other sectors in considering and adopting technology but adopt it will. There are several points across the learner journey where bots are already being used and already a range of fascinating examples.
1.    Onboarding bot
Onboarding is notoriously fickle. New starters in at different times, have different needs and the old model of a huge dump of knowledge, documents and compliance courses is still all too common. Bots are being used to introduce new students or staff to the people, environment and purpose of the organisation. New starters have predictable questions, so answers can be provided straight to mobile, directed to people, processes or procedures, where necessary. It is not that the chatbot will provide the entire solution but it will take the pressure off and respond to real queries as they arise. Available 24/7 it can give access to answer as well as people. What better way to present your organization as innovative and responsive to the needs of students and staff?
2.    FAQ bot
In a sense Google is a chatbot. You type something in and up pops a set of ranked links. Increasingly you may even have a short list of more detailed questions you may want to ask. Straight up FAQ chatbots, with a well-defined set of answers to a predictable set of questions can take the load off customer queries, support desks or learner requests. A lot of teaching is admin and a chatbot can relieve that pressure at a very simple level within a definite domain – frequently asked questions.
3. Invisible LMS bot
At another level, the invisible LMS, fronted by a chatbot, allows people to ask for help and shifts formal courses into performance support, within the workflow. LearningPool’s ‘Otto’ is a good example. It sits on top of content, accessible from Facebook, Slack and other commonly used social tools. You get help in various forms, such as simple text, chunks of learning, people to contact and links to external resources as and when you need them. Content is no longer sits in a dead repository, waiting on you to sign in or take courses, but is a dynamic resource, available when you ask it something.
4. Learner engagement bot
Learners are often lazy. Students leave essays and assignments to the last minute, learners fail to do pre-work, and courses– it’s a human failing. They need prompting and cajoling. Learner engagement bots do this, with pushed prompts to students and responses to their queries. ‘Differ’ from Norway does precisely this. It recognizes that learners need to be engaged and helped, even pushed through the learning journey, and that is precisely what 'Differ' does.
5. Learner support bot
Campus support bots or course support bots go one stage further and provide teaching support in some detail. The idea is to take the administrative load off the shoulders of teachers and trainers. Response times to emails from faculty to students can be glacial. Learner support bots can, if trained well, respond with accurate and consistent answers quickly, 24/7.
The Georgia Tech bot Jill Watson, and its descendants, responds in seconds. Indeed they had to slow its response time down to mimic the typing speed of a human. The learners, 350 AI students, didn’t guess that it was a bot and even put it up for a teaching award.
6. Tutor bots
Tutor bots 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. Straight up teaching is another approach, where the bot behaves like a Socratic teacher, asking sprints of questions and providing encouragement and feedback. This type of bot can be used as a supplement to existing courses to encourage engagement. Wildfire, the AI content generation service uses bots of this type to deliver actual teaching on apprenticeship content, as a supplement to courses, also built using AI, in minutes not months. Once the basic knowledge has been acquired, the bot tests the student as well as getting them to apply their knowledge.
7. Mentor bot
The point of a bot may not be to simply answer questions but to mentor learners by providing advice on how to find the information on your own, to promote problem solving. AutoMentor by Roger Schank,  is one such system, where the bot knows the context and provides, not just FAQ answers but advice. Providing answers is not always the best way to teach. At a higher-level chatbots could be used to encourage problem solving and critical skills, by being truly Socratic, acting as a midwife to the students behaviours and thoughts. Roger Schank is using these in defence-funded projects on Cyber Security.
As the dialogue gets better, drawing not only on a solid knowledge-base, good learner engagement through dialogue, focused 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 could also be possible. Bots 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. This ‘bot as critical companion’ is an interesting line of development.
8. Scenario-based bots
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. Bots can set up problems, prompt through a process, provide feedback and assess effort. Scenarios often involve other people this is where surrogate bots can come in.
9. Practice bots
Practice bots, literally take the role of a customer, patient, learners or any other person and allows learners to practice their customer care, support, healthcare or other soft skills on a responding person (bot). Bots that act as revision bots for exams are also possible.
A bot that mimics someone can be used for practice. For example, the boy with attitude ‘Eli’, developed by Penn State, that mimics an awkward child in the classroom. It is used by student teachers to practice their skills on dealing with such problems before they hit the classroom. Duolingo uses bots after you have gathered an adequate vocabulary, knowledge of grammar and basic competence, to allow practice in a language. This surely makes sense.
10. Wellbeing bots
If a bot is being used in any therapeutic context, its anonymity can be an advantage. From Eliza in the 60s to contemporary therapeutic bots, this has been a rich vein of bot development. There is an example of the word ‘suicidal’ appearing in a student messenger dialogue, that led to a fast intervention, as the student was in real distress. Therapeutic bots are being used in controlled studies to see of they have a beneficial effect on outcomes. Anonymity, in itself, is an advantage in such bots, as the learner may not want to expose their failings.
Bots such as ‘Elli ‘ and ‘Woebot’ are already being subjected to controlled trials to examine the impact on clinical outcomes.
Bot warning
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 idea is that the teacher bot will replace the skills of a teacher, not just be able to tutor in one subject alone, but be a cross-curricular teacher, especially at the higher levels of learning. It could be 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. Let’s be clear, this will not happen any time soon.  AI is nowhere near solving the complex problems that this entails. If someone is promising a bot will replace a teacher – show them the door. Bots will augment not automate teaching.
We have to be careful about overreach here. Effective bots are not easy to build, have to be ‘trained (in AI-speak ‘unsupervised’) and are difficult to build. On the other hand trained bots, with good data sets (in AI-speak ‘supervised’), in specific domains, are eminently possible. 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. Bots fit n more naturally into the xAPI landscape.
Chatbots have real potential in a number of learning activities, all along the learning journey, not as a general; ‘teacher’ but in specific applications within specific domains. They need to be trained, built, tested and improved, which is no easy task, but their efficacy in reducing the workload of teachers, trainers, lecturers and administrators is clear. The dramatic advances in Natural Language Processing have led to Siri, Amazon Echo and Google Home. It is a rapidly developing field of AI and promises to deliver chatbot technology that is better and cheaper by the month.
As a bot 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, albeit limited, 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 seen how online behaviour has moved from flat page-turning (websites) to posting (Facebook, Twitter) to messaging (Txting, Messenger). We have seen 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. 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 bots. But the process of adoption will be gradual.

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 its standard and tries to support and streamline it to produce faster and better outcomes at a lower cost. It takes the admin and pain out of teaching. They are here, more are coming.

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