The difficulty of supercomputers to detect irony, sarcasm, and humor is marked by various attempts that are reduced to superficial solutions based on algorithms that can search factors such as a repetitive use of punctuations marks, use of capital letters or key phrases (Tsur et al. Artificial intelligence solutions relate to tasks that can be automated, but cannot be yet envisaged as a solution for more complex tasks of higher learning.
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We define ‘machine learning’ as a subfield of artificial intelligence that includes software able to recognize patterns, make predictions, and apply the newly discovered patterns to situations that were not included or covered by their initial design.Īs AI solutions have the potential to structurally change university administrative services, the realm of teaching and learning in higher education presents a very different set of challenges. An example is AlphaGo-a software developed by DeepMind, the AI branch of Google’s-that was able to defeat the world’s best player at Go, a very complex board game (Gibney 2017). While some AI solutions remain dependent on programming, some have an inbuilt capacity to learn patterns and make predictions. In this context, it is also important to note that ‘machine learning’ is a promising field of artificial intelligence. A supercomputer able to provide bespoke feedback at any hour is reducing the need to employ the same number of administrative staff previously serving this function. This is changing the structure for the quality of services, the dynamic of time within the university, and the structure of its workforce. Even if it is based on algorithms suitable to fulfill repetitive and relatively predictable tasks, Watson’s use is an example of the future impact of AI on the administrative workforce profile in higher education. This solution provides student advice for Deakin University in Australia at any time of day throughout 365 days of the year (Deakin University 2014). For example, universities already use an incipient form of artificial intelligence, IBM’s supercomputer Watson. Thus, we can define artificial intelligence (AI) as computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks.Īrtificial intelligence is currently progressing at an accelerated pace, and this already impacts on the profound nature of services within higher education. For the purpose of our analysis of the impact of artificial intelligence in teaching and learning in higher education, we propose a basic definition informed by the literature review of some previous definitions on this field.
Most approaches focus on limited perspectives on cognition or simply ignore the political, psychological, and philosophical aspects of the concept of intelligence. However, the variety of definitions and understandings remains widely disputed. Since 1956, we find various theoretical understandings of artificial intelligence that are influenced by chemistry, biology, linguistics, mathematics, and the advancements of AI solutions. It is worth remembering that the focus on AI solutions goes back to 1950s in 1956 John McCarthy offered one of the first and most influential definitions: “The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” (Russell and Norvig 2010). In 1950s, Alan Turing proposed a solution to the question of when a system designed by a human is ‘intelligent.’ Turing proposed the imitation game, a test that involves the capacity of a human listener to make the distinction of a conversation with a machine or another human if this distinction is not detected, we can admit that we have an intelligent system, or artificial intelligence (AI). With answers to the question of ‘what is artificial intelligence’ shaped by philosophical positions taken since Aristotle, there is little agreement on an ultimate definition.
In this field, advances in artificial intelligence open to new possibilities and challenges for teaching and learning in higher education, with the potential to fundamentally change governance and the internal architecture of institutions of higher education. The future of higher education is intrinsically linked with developments on new technologies and computing capacities of the new intelligent machines.
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