In a campus setting students, teachers and support teams seek immediacy. They expect information to be delivered to them quickly and they expect to carry out routine tasks and activities with little or no friction. The advent of the campus chatbot enables schools, colleges and universities to meet this need.
At a simple level, the campus chatbot can respond to day-to-day enquiries about campus services and it can also respond to enquiries that are specific to the individual who engages with the chatbot. In this instance, a student can ask about the lessons that are taking place this morning, the date for a forthcoming exam or the grade awarded for a recent assignment. A teacher can ask for the list of professional development workshops that he has attended over the last year or about the academic performance for a given student on his course.
The ability to garner information in this manner is welcomed. However, as we begin to use chatbots to support individuals across more knowledge domains we soon realise that they need to respond to individuals contextually. Context is important for a variety of reasons. The student who asks about the deadline for a forthcoming assignment needs to know more than just the date and time for submitting the assignment. If the chatbot is aware that the student is seeking to progress on to university after her studies and her grade profile is slightly below target, we should expect the chatbot to advise the student accordingly. In this instance, the chatbot should advise the student about the grade that she needs to secure if she is to maintain her target grade average; or it may advise scheduling a meeting with the course tutor before the submission date.
A chatbots ability to assimilate a large volume of information gives it properties that are useful to students, teachers and support teams; especially when it behaves contextually. The service can nudge, prompt and guide individuals as they engage with services on the campus, and it can support, advise and guide individuals as they carry out day-to-day tasks and activities. This is possible because the chatbot asks itself the following questions when supporting a student: what is the student asking, besides giving the answer is there anything else that I need to be aware of, if so what is it, does it apply to this particular context, how should I respond the student, have previous responses resulted in positive outcomes, which response should I offer, what is the student's position on the student life cycle, do I need to inform teachers and support teams about the question and my response to that question and so on. The campus chatbot recognises the question posed by the student, gathers information, garners insight and presents its answer back to the student with ease; and it repeats this for all students on the campus on a daily basis throughout the academic year.
At an operational level, a student could have numerous agents acting on her behalf. These agents will have specific roles such as supporting the student to secure her place at university, making sure that she meets her target grade, recommending reading lists or reminding the student about her assignment deadlines. These agents inform the behaviour of the primary chatbot on the campus; even controlling the tone of messages or conversations with the student.
When campus chatbots become context aware they are in a better position to help students, teachers and support teams with the myriad of tasks and activities that need to be done on a daily basis in our schools, colleges and universities. I don't expect campus chatbots to be an exact facsimile of a teacher or a member of the student support team; but as these services become more context aware colleagues will increasingly use them to support their workload. Indeed, the success of any campus chatbot is dependent on the support that teachers and other colleagues offer when training the chatbot.