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This is not science fiction, rather it is science fact that we increasingly are sending Artificial Intelligent (AI) agents on our personal and professional tasks.

We have discussed much about the development of AI capabilities in this column. We have reached the point of near-vertical expansion of AI speed, capacity and scope of knowledge. Elon Musk is convinced that OpenAI and Microsoft have already achieved Artificial General Intelligence. If that is not the case, it seems that AGI will soon be achieved and the summative force of research will be applied to accomplishing Artificial Super Intelligence (ASI). What is ASI, you ask? IBM says “Artificial superintelligence (ASI) is a hypothetical software-based artificial intelligence (AI) system with an intellectual scope beyond human intelligence. At the most fundamental level, this superintelligent AI has cutting-edge cognitive functions and highly developed thinking skills more advanced than any human.” This is a step above AGI. It includes abilities well beyond those of a single human. It surpasses the “knowledge and abilities” of collective humanity.

ASI is expected to also possess both cognitive and affective characteristics. Affective computing extends beyond just facts and logic to encompass human emotions more fully:

Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human emotions. In the AI context, the significance of affective computing lies in its ability to bridge the gap between human emotions and technological advancements, thereby enhancing the overall user experience and interaction with AI-driven systems. By imbuing AI with emotional intelligence, affective computing contributes to creating more empathetic, responsive, and adaptive technology. The importance of affective computing in AI is underscored by its potential to revolutionize various sectors, including healthcare, education, customer service, and entertainment. By understanding and responding to human emotions, AI systems can personalize experiences, offer tailored support, and facilitate more natural and intuitive human-machine interactions, thereby significantly augmenting the utility and acceptance of AI technologies.

In some cases, this is referred to as sentient computing:

We can use this model to write programs that react to changes in the environment according to the user’s preferences. We call this sentient computing because the applications appear to share the user’s perception of the environment. Treating the current state of the environment as common ground between computers and users provides new ways of interacting with information systems.

We don’t need all of the above characteristics to launch personal intelligent agents. However, when we do combine these qualities and characteristics, we are able to most fully accomplish autonomous agents that can make and exchange ideas and perform tasks on our behalf while retaining our personal values, orientations and knowledge. To further speed the actions and responses, we may be running these agents on quantum computing platforms.  As Johnathan Reichental writes in Forbes “The deliberate collision of two game-changing technologies has the potential to upend the technology industry and bring about a new era of business disruption and innovation. Few industries will be spared this transformation, and it will create completely new value and risks. Hyperbole? I don’t think so. In the future, artificial intelligence is likely to become supercharged by quantum computing. It’s a partnership that could change the world.”

Each of these qualities will be accomplished in steps, not necessarily sequentially in the order that we have discussed them, but calling upon the addition of these distinct and unique computing characteristics. What will this mean for higher education, learning and personal application of knowledge and skills?

In short, it means we humans will be able to virtually replicate and extend our personae in multiple simultaneous forms around the world. Our agents will be in many places simultaneously. Our AI-extended knowledge, values and personalities will be omni-present. Imagine the implications for exceptional faculty members in their roles as teachers and researchers!

One early model is “Devin” who can be tasked with software engineering. Developed by Cognition, Devin is, as Will Knight describes in Wired “Devin is just the latest, most polished example of a trend I’ve been tracking for a while—the emergence of AI agents that instead of just providing answers or advice about a problem presented by a human can take action to solve it.”

For the sake of higher education, I expect that learning will be offered in adaptive and mastery formats. That means as students are engaged in learning, they will be constantly assessed for knowledge of the particular aspect of the subject they are studying at the time. These assessments will be constructed such that the student’s response will be instantly analyzed to determine any wrong answers that are given as well as the misconceptions that led them to submit the wrong answer. Students, then, are diverted to a module with tutoring addressing the misconception until they understand the correct approach. Historically, faculty have not had the tools and time to conduct such analyses and remediate students within the semester. Thus, with few exceptions, we have not been able to ensure that all students have achieved full mastery of the material in the course.

Using the computer to serve as a kind of tutor/agent in assessing and redirecting students will lead to mastery learning as the standard approach in higher education. In my opinion, this will be one of the greatest advances of all time in pedagogy and practice. I have always felt uncomfortable with awarding a C, D, or F to work in a course. We should not move a student forward until they fully master the material of the class. In many cases, we scaffold our learning across multiple classes with each succeeding class depending upon the full knowledge, understanding and skill in applying the material of the prior class. That scaffold is compromised if, at any level, our students fail to fully learn the material. An entire degree, and even a career, can be compromised by one faulty concept learned, or incompletely learned, in an introductory class! Of course, this means individual semesters may be shorter or longer for each student than the rigid number of weeks today as students take more or less time to master the material. However, the outcomes will be uniformly strong or better. We should accept nothing less.

We are on the brink of raising the bar of quality and completeness in all of higher education with the aid of artificial intelligence. Is your institution prepared for the changes that will come in the months and years ahead? Who is providing the forward-thinking vision, and leading the planning required to implement these changes?

This article was originally published in Inside Higher Ed’s Transforming Teaching & Learning blog.