State of the art: AI and Big Data

AI has been a topic on the radar of theorists and experts since the 1950s, and, to this day, no agreement has been reached as to its definition. Studies into AI began in 1956 when John McCarthy used the term at a seminar at Dartmouth University in the United States. Prior to McCarthy, starting in 1951, research studies on genetics within the field of biological sciences had also considered AI. Also, in 1950, Alan Turing published the study “Computing Machinery and Intelligence” where he presented the “Imitation Game” also known as the “Turing Test”, which is a set of questions aimed at assessing whether the respondent is a human or a machine.

Russel and Norvig (1995) explored AI in four categories: systems that think like humans; systems that act like humans; systems that think rationally; and systems that act rationally. In the history of the study of AI, the four categories welcome theorists and followers, finding tensions at their edges between studies centered on “Humanity” or “Rationality”.

Publications from 2014 reflected these distinctions on how AI can be applied. The International Telecommunication Union (ITU) released the reports “Artificial Intelligence for Development Series” (2017) and “AI for Good Summit” (2017 and 2018); where it described the developments in the concept of a system that does not replace human intelligence. Similarly, the Organization for Economic Co-operation and Development (OECD) in partnership with International Business Machines (IBM) in the document “AI: Intelligent machines, smart policies” (2018) positions AI as a structure that increases the potential of human intelligence.

Floridi (2014), on the other hand, discussed the applications of AI arguing that successful systems are those with a molded environment around them. In other words, systems that respond to specific purposes perform best —the author gives an example of a robot which may cut grass well is unlikely to also perform the role of a refrigerator just as well. This is known as a “frame problem”. According to Floridi (2014), AI does not have a descriptive or prescriptive approach to the world; rather, it assesses the logical and mathematical coercion that makes it possible to construct artifacts and interact with them effectively.

The United Nations on the other hand, in their publication “Innovative Big Data approaches for capturing and analyzing data to monitor and achieve the SDGs” (2017) acknowledged that defining AI is not a straightforward task. The initial problem is to define what “intelligence” means or which intelligence is executed by humans and non-humans. Despite various attempts, none of the involved disciplines, such as psychology or education science, has come up with a satisfying, mutually agreed upon definition of intelligence. Legg and Hutter (2018) provided an overview of the many definitions proposed over the years: “Intelligence measures an agent’s ability to achieve goals in a wide range of environments”. In terms of AI, the “agent” in this definition could be a human being (regular intelligence) or a system (AI). As such, a machine that exhibits intelligence, which equals human intelligence, can be referred to as having general AI.

Connection between AI and Big Data

AI happenedin the wake of the Zettabyte Era, which means that an intelligent performance from a machine is required as a consequence of Big Data. Generations since 2014 have experienced the Zetta flood that describes the byte tsunami that is taking over the environment where we live. While AI has become a natural development of an intelligent system that needs to deal with Big Data, this is why the terms are structurally connected. Despite the importance of the phenomenon, the definition of Big Data is still unclear. The term was first introduced in 1989 by Erik Larson in a piece published by The Washington Post on how to deal with junk mail. However, theorists attribute the concept of Big Data as we use it today to John R. Mashey’s “Big Data and the next wave of infrastress” published in 1998, where he acknowledges a field that requires high capacity in running analytic models to cope with vast amounts of data. Many authors have contributed to the development of the term, and in 2012 one of the first legal regulations of public and sensitive data was launched.

The current law of the General Data Protection Regulation (GDPR - European Union Commission, 2020) contains legal requirements for the use of personal data for historical, statistical and scientific research purposes effective worldwide. Floridi (2014), who contributed to the launch of GDPR, warns of two common mistakes when talking about Big Data: one is misunderstanding “Big” to refer to physical size, and the other is misunderstanding the “Data” as being “Big” only when in perspective to computational power. These two mistakes, in turn, have two different sources: the epistemological problem when thinking that there is too much data; and the idea that the solution to this problem is technological (as if technology could synthesize/reduce data amount). The confusion lies in the fact that an epistemological problem requires an epistemological solution, rather than a technical one.

The problems in Big Data interpretation and basic features

Since the problem is not the increasing amount of data, the solutions need to be updated. It is not about our processing capacity (since this activity happens on demand), but about the epistemological question of small patterns analyzing Big Data. Small patterns represent a new frontier of innovation and competition, from science to business, from governance to social policies, from security to protection. The reason why patterns should be small is to improve their processing speed —since there is a large quantity of data, the small patterns group them to speed up their synthesis). A potential ethical issue concerning the use of small patterns stems from their ability to predict future events, as they can foresee choices and behaviors, which comes up against ethical principles of information.

Another feature of data is related to volume, and the Cloud Security Alliance (2014) in its report “Big Data Taxonomy” introduces three limits to the growing usage and storage of data: thermodynamics, intelligence and memory. This provides a concerning counterweight to the argument that AI is a solution to Big Data, since intelligence is one of Big Data’s limitations. Big Data responds to data acquisition and storage, and humanity has not produced enough storage for the data we are producing, which is therefore a limitation in terms of memory.

Søe (2018) warns, however, that the main problem with this particular field is epistemological. The perspective of the amount of data being a problem is misleading, once the main question is how late people became aware of Big Data’s existence. This analysis goes in the opposite direction of the idea of data as sets so large and complex that they become difficult to process using available database management tools for traditional data processing applications, which brings us to another misleading conception as mentioned above: weak computational power. But why are small patterns such a big issue? Floridi, in a lecture in 2018 at Oxford Internet Institute, answered that question with a dot connection figure illustration: the more data points you have, the better the pattern must be, and unless you connect all the dots you will not see what the figure is really about. The question of Big Data is that, among zetta bytes of information, a pattern is required in order to conduct an analysis. Floridi compared it to finding a needle in a haystack. The integration between Big Data and AI is that data groups must create their own intelligence to identify the needle.

Yet, not every piece of data is important. Mantelero (2018) points out that perhaps half our data is insignificant, while the other half is valuable. The role of small patterns is to know which half is required. Once the valuable assets are mapped, and the needles found, an aggregation feature could be considered. This means that combining important data may drive a system to understand their customer and even to predict their choices. Thus, small patterns, as a methodological procedure, are significant when they correlate relevant data including the absence of data itself.

Collective intelligence and its ability to move forward with AI algorithms depend on a colossal historical database capable of generating insights into predictive behaviors and educational outcomes. Yet, one of the major challenges for the advancement of AI is the systematization and organization of useful data.

21^st century competencies: Mixed methods to apply and evaluate them

The platform society has the potential to demand new competencies from citizens, which brings the discussion to 21st century competencies. It is necessary to clarify concepts such as competence, capacity, capability, ability and skill, considering that these words are generally used interchangeably. Some definitions from Oxford English Dictionary can help to address this task:

• Ability: The fact that somebody/something is able to do something.

• Capability: The ability or qualities necessary to do something

• Competence: The ability to do something well.

• Knowledge: The information, understanding and skills that you gain through education or experience.

• Literacy: The ability to creatively and culturally read and write on any surface.

• According to this dictionary, ability, capacity and capability are synonyms, as well as skill and competence.

• The O'Net database defines the terms as follows.

• Ability: enduring attributes of the individual that influence performance (cognitive, physical, psychomotor and sensory abilities).

• Skills: developed capacities that facilitate learning or the more rapid acquisition of knowledge (complex problem solving, resource management, social, system and technical skills).

According to Zabala and Arnau (2015), competence is defined as the capacity or ability (which means having the cognitive structure) to perform tasks and engage in diverse situations such as the ones from political, social and cultural life, in an effective and conscious way and adapted to a certain context. It is necessary to mobilize attitudes, skills and knowledge orchestrating and interrelating them. Similarly, the OECD (Rychen & Salganik, 2003) defined competence as the ability to successfully meet complex demands in a particular context through the mobilization of knowledge, skills, attitudes, and values. In this case, values were added as a new element to the construct. In this article, we will use the umbrella concept of competence from the OECD, in which skills will be considered a subset of competence. Ability, capability and capacity will be considered synonyms exactly as defined by the OECD.

The emergence of proposing frameworks for 21st century education structures a globalized and technologically driven mindset in the information age. For instance, Fadel and Groff (2019) proposed the four-dimensional education model in which knowledge, skills, character, and meta-learning competencies are dimensions that need to be explored to successfully redesign a curriculum. The traditional mindset of thinking and designing a curriculum is centered on knowledge transfer. Wilson (1999) points out that humankind is drowning in information while starving for wisdom. The world henceforth will be run by synthesizers. These are people who are able to put together the right information at the right time, think critically about it, and make important choices wisely. Next, we will present three methodological cohorts that can help explain the interface between Big Data, AI and Education: research, application and assessment.

Research, application and assessment

Experiments in education necessarily entail the systematic study of particular forms of learning. This context undergoes research, test and revision; and in this research, we offer netnography as a research method, followed by three examples of application that may be seen by the methods Competency Based Education (CBE), the Four-dimensional Model and the OECD Compass Model.