Over the last two years, machines have demonstrated their ability to read, listen, and understand English well enough to beat the best at Jeopardy!, answer questions via iPhone, and earn college credit on college advanced placement exams.\u00a0 Today, Google, Microsoft and others are rushing to respond to IBM and Apple with ever more competent artificially intelligent systems that answer questions and support decisions.<\/p>\n
Even excluding political dynamics, these are turbulent times for education, including for academic institutions, textbook publishers, and educational technology providers. Ubiquitous computing on increasingly capable phone, tablet, and cloud-computing infrastructures in an increasingly open environment are driving courses and textbooks on-line and onto hand-held devices.\u00a0 As if these disruptions are not overwhelming in themselves, the incorporation of artificial intelligence within emergent educational technology promises further disruption albeit with innovation that actually improves learning beyond \u201cmerely\u201d democratizing education.<\/p>\n
Consider a machine that \u201cunderstands\u201d its content.\u00a0 In Project Halo, we found that educating a machine <\/a>such that it could get college credit in the sciences cost on the order of $10,000 per age a decade ago.\u00a0 Over time, using graphical metaphors<\/a>, the cost per page was reduced to a few thousand dollars. And, more recently, by understanding English using the Linguist\u2122<\/a>, the cost has fallen by two orders of magnitude.<\/p>\n A page typically contains less than 25 sentences of well under 20 words in length each, on average.\u00a0 Using statistical natural language processing, these sentences are too complex to be precisely understood, although evidentiary approaches, such as demonstrated in Watson<\/a>, suggest that most can be understood with sufficient precision to answer many questions.\u00a0 As the questions become more difficult, however, such as on AP exams or whenever several steps of inference need to be \u201cchained\u201d together, it becomes necessary to understand more sentences more precisely.\u00a0 Today, understanding a sentence accurately requires a human being to resolve various ambiguities that induction alone cannot.<\/p>\n To disambiguate a typical textbook sentence now requires somewhere between seconds and minutes.\u00a0 Doing so results in understanding more of the subject matter more deeply and more accurately than Watson can.\u00a0 To do this for a page requires an hour or so.\u00a0 The result is artificial intelligence that can answer questions that are far more difficult than Watson can answer and doing so reliably rather than guessing.\u00a0 Moreover, the reasoning technology involved can explain the chain of inference and relate it to the words, phrases, and sentences from which the knowledge was acquired.\u00a0 (In today\u2019s electronic publishing platforms and delivery vehicles, such indexing and richer navigation is done manually.) And the skill required to help the machine understand subject matter is widely available.\u00a0 People who contribute to Wikipedia are able to clarify the meaning of their text using the Linguist on day 1.<\/p>\n Consider another perspective.\u00a0 Question banks are commonly used in education and in test preparation.\u00a0 Question bank contributors can now add the sentences of knowledge needed to prove or refute answers.\u00a0 By assisting the machine in understanding those sentences, the machine is able not only to answer such questions on its own but to assess and assist learners in understanding the subject matter such that they do better, too.<\/p>\n This is actually easier than it may seem, as has been demonstrated by a prototypical electronic book<\/a> that lifted the test scores<\/a> of college biology students.\u00a0 In that case, the machine simply presented suggested questions that students could answer and through which students could navigate through relevant textbook content.\u00a0 In effect, the textbook integrated a question bank for which the machine had pre-computed the proofs and refutations of correct or incorrect answers.\u00a0 The logical semantics of these proofs was linked\u00a0 to the textbook content and as the basis for automatically rendered conceptual summaries.<\/p>\n Many textbooks generate millions of dollars of revenue per annum.\u00a0 And yet these textbooks are vulnerable to increasingly open publishing of electronic text.\u00a0 Although electronic textbooks offer more value, convenience, and functionality than the paper can offer, they are generally passive with regard to learning.\u00a0 Electronic textbook delivery platforms that incorporate active, adaptive learning such as enabled by machine understood content and artificially intelligent question answering, assessment, and pedagogical capabilities offer significantly more value to learners that either form of textbook alone.\u00a0 Now, a relatively small incremental investment can secure competitive distinction that avoids commoditization by electronic publishing.\u00a0 Thus, all publishers, including copyright holders and delivery channels are motivated to adopt understanding.<\/p>\n But what are the implications for tomorrow?\u00a0 Will textbooks be written in a more knowledge-driven manner rather than as sequential discourse?\u00a0 Perhaps.\u00a0 But perhaps that\u2019s the wrong question.\u00a0 What clearly matters is whether learners learn better.\u00a0 There can be little doubt that content that understands itself and artificial intelligence that answers and explains questions better than Watson will engage and teach learners better.<\/p>\n","protected":false},"excerpt":{"rendered":" Over the last two years, machines have demonstrated their ability to read, listen, and understand English well enough to beat the best at Jeopardy!, answer questions via iPhone, and earn college credit on college advanced placement exams.\u00a0 Today, Google, Microsoft and others are rushing to respond to IBM and Apple with ever more competent artificially … <\/p>\nWhat are the implications for educational technology markets?<\/h3>\n