Automatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from water meter or energy metering devices (gas, electric) and transferring that data to a central database for billing, troubleshooting, and analyzing. This technology mainly saves utility providers the expense of periodic trips to each physical location to read a meter. Another advantage is that billing can be based on near real-time consumption rather than on estimates based on past or predicted consumption. This timely information coupled with analysis can help both utility providers and customers better control the use and production of electric energy, gas usage, or water consumption. AMR technologies include handheld, mobile and network technologies based on telephony platforms (wired and wireless), radio frequency (RF), or powerline transmission. == Technologies == === Touch technology === With touch-based AMR, a meter reader carries a handheld computer or data collection device with a wand or probe. The device automatically collects the readings from a meter by touching or placing the read probe close to a reading coil enclosed in the touchpad. When a button is pressed, the probe sends an interrogate signal to the touch module to collect the meter reading. The software in the device matches the serial number to one in the route database, and saves the meter reading for later download to a billing or data collection computer. Since the meter reader still has to go to the site of the meter, this is sometimes referred to as "on-site" AMR. Another form of contact reader uses a standardized infrared port to transmit data. Protocols are standardized between manufacturers by such documents as ANSI C12.18 or IEC 61107. === AMR hosting === AMR hosting is a back-office solution which allows a user to track their electricity, water, or gas consumption over the Internet. All data is collected in near real-time, and is stored in a database by data acquisition software. The user can view the data via a web application, and can analyze the data using various online analysis tools such as charting load profiles, analyzing tariff components, and verify their utility bill. === Radio frequency network === Radio frequency based AMR can take many forms. The more common ones are handheld, mobile, satellite and fixed network solutions. There are both two-way RF systems and one-way RF systems in use that use both licensed and unlicensed RF bands. In a two-way or "wake up" system, a radio signal is normally sent to an AMR meter's unique serial number, instructing its transceiver to power-up and transmit its data. The meter transceiver and the reading transceiver both send and receive radio signals. In a one-way "bubble-up" or continuous broadcast type system, the meter transmits continuously and data is sent every few seconds. This means the reading device can be a receiver only, and the meter a transmitter only. Data travels only from the meter transmitter to the reading receiver. There are also hybrid systems that combine one-way and two-way techniques, using one-way communication for reading and two-way communication for programming functions. RF-based meter reading usually eliminates the need for the meter reader to enter the property or home, or to locate and open an underground meter pit. The utility saves money by increased speed of reading, has less liability from entering private property, and has fewer missed readings from being unable to access the meter. The technology based on RF is not readily accepted everywhere. In several Asian countries, the technology faces a barrier of regulations in place pertaining to use of the radio frequency of any radiated power. For example, in India the radio frequency which is generally in ISM band is not free to use even for low power radio of 10 mW. The majority of manufacturers of electricity meters have radio frequency devices in the frequency band of 433/868 MHz for large scale deployment in European countries. The frequency band of 2.4 GHz can be now used in India for outdoor as well as indoor applications, but few manufacturers have shown products within this frequency band. Initiatives in radio frequency AMR in such countries are being taken up with regulators wherever the cost of licensing outweighs the benefits of AMR. ==== Handheld ==== In handheld AMR, a meter reader carries a handheld computer with a built-in or attached receiver/transceiver (radio frequency or touch) to collect meter readings from an AMR capable meter. This is sometimes referred to as "walk-by" meter reading since the meter reader walks by the locations where meters are installed as they go through their meter reading route. Handheld computers may also be used to manually enter readings without the use of AMR technology as an alternate but this will not support exhaustive data which can be accurately read using the meter reading electronically. ==== Mobile ==== Mobile or "drive-by" meter reading is where a reading device is installed in a vehicle. The meter reader drives the vehicle while the reading device automatically collects the meter readings. Often, for mobile meter reading, the reading equipment includes navigational and mapping features provided by GPS and mapping software. With mobile meter reading, the reader does not normally have to read the meters in any particular route order, but just drives the service area until all meters are read. Components often consist of a laptop or proprietary computer, software, RF receiver/transceiver, and external vehicle antennas. ==== Satellite ==== Transmitters for data collection satellites can be installed in the field next to existing meters. The satellite AMR devices communicate with the meter for readings, and then sends those readings over a fixed or mobile satellite network. This network requires a clear view to the sky for the satellite transmitter/receiver, but eliminates the need to install fixed towers or send out field technicians, thereby being particularly suited for areas with low geographic meter density. ==== RF technologies commonly used for AMR ==== Narrow Band (single fixed radio frequency) Spread spectrum Direct-sequence spread spectrum (DSSS) Frequency-hopping spread spectrum (FHSS) There are also meters using AMR with RF technologies such as cellular phone data systems, Zigbee, Bluetooth, Wavenis and others. Some systems operate with U.S. Federal Communications Commission (FCC) licensed frequencies and others under FCC Part 15, which allows use of unlicensed radio frequencies. ==== Wi-Fi ==== WiSmart is a versatile platform which can be used by a variety of electrical home appliances in order to provide wireless TCP/IP communication using the 802.11 b/g protocol. Devices such as the Smart Thermostat permit a utility to lower a home's power consumption to help manage power demand. The city of Corpus Christi became one of the first cities in the United States to implement citywide Wi-Fi, which had been free until May 31, 2007, mainly to facilitate AMR after a meter reader was attacked by a dog. Today many meters are designed to transmit using Wi-Fi, even if a Wi-Fi network is not available, and they are read using a drive-by local Wi-Fi hand held receiver. The meters installed in Corpus Christi are not directly Wi-Fi enabled, but rather transmit narrow-band burst telemetry on the 460 MHz band. This narrow-band signal has much greater range than Wi-Fi, so the number of receivers required for the project are far fewer. Special receiver stations then decode the narrow-band signals and resend the data via Wi-Fi. Most of the automated utility meters installed in the Corpus Christi area are battery powered. Wi-Fi technology is unsuitable for long-term battery-powered operation. === Power line communication === PLC is a method where electronic data is transmitted over power lines back to the substation, then relayed to a central computer in the utility's main office. This would be considered a type of fixed network system—the network being the distribution network which the utility has built and maintains to deliver electric power. Such systems are primarily used for electric meter reading. Some providers have interfaced gas and water meters to feed into a PLC type system. == Brief history == In 1972, Theodore George "Ted" Paraskevakos, while working with Boeing in Huntsville, Alabama, developed a sensor monitoring system which used digital transmission for security, fire and medical alarm systems as well as meter reading capabilities for all utilities. This technology was a spin-off of the automatic telephone line identification system, now known as caller ID. In 1974, Paraskevakos was awarded a U.S. patent for this technology. In 1977, he launched Metretek, Inc., which developed and produced the first fully automated, commercially available remote meter reading and load management system. Since this system was developed pre-Internet, Metret
AI literacy
AI literacy or artificial intelligence literacy is "a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace." AI is employed in a variety of applications, including self-driving automobiles, virtual assistants and text generation by generative AI models. Users of these tools should be able to make informed decisions. AI literacy may have an impact on students' future employment prospects. With the rise of generative AI platforms, AI literacy has become a topic of conversation in the field of education. Some think AI literacy is essential for school and college students, while others restrict or prohibit the use of AI in assignments, viewing it as a form of academic dishonesty. However, many researchers and educational institutions promote a more nuanced approach, encouraging critical engagement with AI while developing policies that balance academic integrity with opportunities for learning. == Definitions == Other definitions of AI literacy include the ability to understand, use, monitor, and critically reflect on AI applications. That use of the term usually refers to teaching skills and knowledge to the general public, particularly those who are not adept in AI and the ability to understand, use, evaluate, and ethically navigate AI. As research into AI literacy is still emerging and focused on developing context-specific skills, there is not yet a single, broadly agreed-upon definition. AI literacy is linked to other forms of literacy. AI literacy requires digital literacy, whereas scientific and computational literacy may inform it. Data literacy also significantly overlaps with it. == Categories == AI literacy encompasses multiple categories, including a theoretical understanding of how artificial intelligence works, the usage of artificial intelligence technologies, and the critical appraisal of artificial intelligence, and its ethics. === Know and understand AI === Knowledge and understanding of AI refers to a basic understanding of what artificial intelligence is and how it works. This includes familiarity with machine learning algorithms and the limitations and biases present in AI systems. Users who know and understand AI should be familiar with various technologies that use artificial intelligence, including cognitive systems, robotics and machine learning. This includes recognizing that large language models (LLMs) are machine learning models trained on extensive datasets which generate new text rather than retrieving pre-written responses. === Use and apply AI === Using and applying AI refers to the ability to use AI tools to solve problems and perform tasks such as programming and analyzing big data. Some consider prompt engineering, the practice of designing effective prompts to guide generative AI platforms more effectively, as another competency within AI literacy. === Evaluate and create AI === Evaluation and creation refers to the ability to critically evaluate the quality and reliability of AI systems. It also refers to designing and building fair and ethical AI systems. To evaluate correctly, users should also learn in which areas AI is strong, and in which areas it is weak. === AI ethics === AI ethics refers to understanding the moral implications of AI, and the making informed decisions regarding the use of AI tools. This area includes considerations such as: Accountability: Hold AI actors accountable for the operation of AI systems and adherence to ethical ideals. Accuracy: Identify and report sources of error and uncertainty in algorithms and data. Auditability: Enable other parties to audit and assess algorithm behavior via transparent information sharing. Explainability: Make sure that algorithmic judgments and the underlying data can be presented in simple language. Fairness: Prevent biases and consider varied viewpoints. To do so, increase the diversity of researchers in the field. Human Centricity and Well-being: Prioritize human well-being in AI development and deployment. Human rights Alignment: Ensure that technology do not infringe internationally recognized human rights. Inclusivity: Make AI accessible to everyone. Progress: Choose high value initiatives. Responsibility, accountability, and transparency: Foster trust via responsibility, accountability, and fairness. Robustness and Security: Make AI systems safe, secure, and resistant to manipulation or data breach. Sustainability: Choose implementations that generate long-term, useful benefits. Environmental Implications: How this tool impacts the environment, any restrictions or laws, if this impact is worth the effects or not. === Enabling AI === Support AI by developing associated knowledge and skills such as programming and statistics. == Promoting AI literacy == Several governments have recognized the need to promote AI literacy, including among adults. Such programs have been published in the United States, China, Germany and Finland. Programs intended for the general public usually consist of short and easy to understand online study units. Programs intended for children are usually project-based. Programs for students at colleges and universities often address the specific professional needs of the student, depending on their field of study. Beyond the education system, AI literacy can also be developed in the community, for example in museums. === Schools === Schools use diverse pedagogies to promote AI literacy. These include: Performing a Turing test with an intelligent agent Creating chatbots Building apps using Blockly-based programming Project-based learning Building robots Data visualization Training AI models Artificial intelligence curricula can improve students' understanding of topics such as machine learning, neural networks, and deep learning. === Higher education === Before the second decade of the 21st century, artificial intelligence was studied mainly in STEM courses. Later, projects emerged to increase artificial intelligence education, specifically to promote AI literacy. Most courses start with one or more study units that deal with basic questions such as what artificial intelligence is, where it comes from, what it can do and what it can't do. Most courses also refer to machine learning and deep learning. Some of the courses deal with moral issues in artificial intelligence. In Ireland, the Higher Education Authority published Generative AI in Higher Education Teaching & Learning: Policy Framework in December 2025, which encouraged higher education institutions to embed AI literacy across programmes as a core graduate attribute. ==== Disciplinary policy ==== As a response to the increase of generative AI use in education, several disciplines formed committees or task forces to examine context-specific approaches toward AI literacy. In spring 2025, the Modern Language Association and Conference on College Composition and Communication Joint Task Force finished development of three working papers, a guide on AI literacy for students, and a collection of resources addressing AI use in writing. The task force emphasized the need for "a culture of critical AI literacy" and included guidelines not only for students but also educators and institutions, highlighting the need for modeling ethical AI use in planning processes. Similarly, a committee formed by the American Historical Association Council published "Guiding Principles for Artificial Intelligence in History Education" which encouraged "clear and transparent engagement with generative AI." The guidelines demonstrate the value of criticality when working with generative AI in thinking and research.
Innovation Center for Artificial Intelligence
The Innovation Center for Artificial Intelligence (ICAI) is a Dutch national network focused on joint technology development between academia, industry and government in the area of artificial intelligence (AI). The initiative was launched in April 2018 and is based at Amsterdam Science Park. As of 2024, the director of the ICAI is Maarten de Rijke. In November 2018, ICAI announced its contribution to AINED, the first iteration of the Dutch National AI Strategy. In January 2023, Maastricht University announced the ROBUST program, led by the Innovation Center for Artificial Intelligence (ICAI) and supported by the University of Amsterdam and others. This initiative focuses on advancing research in trustworthy AI technology across various sectors, notably healthcare and energy, in the Netherlands. The program's plan includes the creation of 17 new labs and the appointment of PhD candidates, backed by a €25 million funding from the Dutch Research Council (NWO). == Labs == The ICAI network is linked to several collaborative labs: Thira Lab (Imaging): Thirona, Delft Imaging Systems and Radboud UMC, founded March 2019 AIMLab (AI for Medical Imaging): Uva and Inception Institute of Artificial Intelligence from the United Arab Emirates, founded March 2019 AFL (AI for Fintech): ING and Delft University of Technology, founded March 2019 Police Lab AI: Dutch National Police, founded January 2019 Elsevier AI Lab: Uva and Elsevier, founded October 2018 AIRLab Delft (AI for Retail Robotics): TU Delft Robotics and AholdDelhaize, founded November 2018 Quva Lab (Deep Vision): Uva and Qualcomm, founded 2016 (prior to ICAI) AIRLab Amsterdam (AI for Retail): Uva and AholdDelhaize, founded April 2018 DeltaLab (Deep Learning Technologies Amsterdam): Uva and Bosch, founded April 2017 (prior to ICAI) AI4SE (AI for Software Engineering Lab) Delft University of Technology and JetBrains, founded October 2023 Atlas Lab: Uva and TomTom (TOM2)
Generative AI Copyright Disclosure Act
The Generative AI Copyright Disclosure Act is a piece of legislation introduced by California Representative Adam Schiff in the United States Congress on April 9, 2024. It concerns the transparency of companies regarding their use of copyrighted work to train their generative artificial intelligence (AI) models. The legislation requires the submission of a notice regarding the identity and the uniform resource locator (URL) address of the copyrighted works used in the training data to the Register of Copyrights at least 30 days before the public release of the new or updated version of the AI model; it does not ban the use of copyrighted works for AI training. The bill's requirements would apply retroactively to prior AI models. Violation penalties would start at US$5,000. The legislation does not have a maximum penalty assessment that can be charged. The bill by Schiff was introduced a few days after The New York Times published an article regarding the business activities of major tech firms, including Google and Meta, in the training of their generative AI platforms on April 6, 2024. The legislation is supported by the Professional Photographers of America (PPA), SAG-AFTRA, the Writers Guild of America, the International Alliance of Theatrical Stage Employees (IATSE), the Recording Industry Association of America (RIAA), and others.
Fooocus
Fooocus is an open source generative artificial intelligence program that allows users to generate images from a text prompt. It uses Stable Diffusion XL as the base model for its image capabilities as well as a collection of default settings and prompts to make the image generation process more streamlined. == History == Fooocus was created by Lvmin Zhang, a doctoral student at Stanford University who previously studied at the Chinese University of Hong Kong and Soochow University. He is also the main author of ControlNet, which has been adopted by many other Stable Diffusion interfaces, such as AUTOMATIC1111 and ComfyUI. As of 9 July 2024, the project had 38.1k stars on GitHub. == Features == Fooocus' main feature is that it is easy to set up and does not require users to manually configure model parameters to achieve desirable results. According to the project, it uses GPT-2 to automatically add more detail to the user's prompts. It includes common extensions such LCM low-rank adaptation by default which allows for faster generation speed. Fooocus prefers a photographic style by default, with a list of predefined styles to choose from. While Fooocus aims to provide good results out of the box, it also includes an "advanced" tab that allows for user customization. The user interface is based on Gradio. It appears this project has not been updated in over 1 year. The latest git update for Fooocus was in Aug 12, 2024.
Steerable filter
In image processing, a steerable filter is an orientation-selective filter that can be computationally rotated to any direction. Rather than designing a new filter for each orientation, a steerable filter is synthesized from a linear combination of a small, fixed set of "basis filters". This approach is efficient and is widely used for tasks that involve directionality, such as edge detection, texture analysis, and shape-from-shading. The principle of steerability has been generalized in deep learning to create equivariant neural networks, which can recognize features in data regardless of their orientation or position. == Example == A common example of a steerable filter is the first derivative of a two-dimensional Gaussian function. This filter responds strongly to oriented image features like edges. It is constructed from two basis filters: the partial derivative of the Gaussian with respect to the horizontal direction ( x {\displaystyle x} ) and the vertical direction ( y {\displaystyle y} ). If G ( x , y ) {\displaystyle G(x,y)} is the Gaussian function, and G x {\displaystyle G_{x}} and G y {\displaystyle G_{y}} are its partial derivatives (which measure the rate of change in the x {\displaystyle x} and y {\displaystyle y} directions, respectively), a new filter G θ {\displaystyle G_{\theta }} oriented at an angle θ {\displaystyle \theta } can be synthesized with the formula: G θ = cos ( θ ) G x + sin ( θ ) G y {\displaystyle G_{\theta }=\cos(\theta )G_{x}+\sin(\theta )G_{y}} Here, the basis filters G x {\displaystyle G_{x}} and G y {\displaystyle G_{y}} are weighted by cos ( θ ) {\displaystyle \cos(\theta )} and sin ( θ ) {\displaystyle \sin(\theta )} to "steer" the filter's sensitivity to the desired orientation. This is equivalent to taking the dot product of the direction vector ( cos θ , sin θ ) {\displaystyle (\cos \theta ,\sin \theta )} with the filter's gradient, ( G x , G y ) {\displaystyle (G_{x},G_{y})} . == Generalization in deep learning: Equivariant neural networks == The concept of steerability is foundational to equivariant neural networks, a class of models in deep learning designed to understand symmetries in data. A network is considered equivariant to a transformation (like a rotation) if transforming the input and then passing it through the network produces the same result as passing the input through the network first and then transforming the output. Formally, for a transformation T {\displaystyle T} and a network f {\displaystyle f} , this property is defined as f ( T ( input ) ) = T ( f ( input ) ) {\displaystyle f(T({\text{input}}))=T(f({\text{input}}))} . This built-in understanding of geometry makes models more data-efficient. For example, a network equivariant to rotation does not need to be shown an object in multiple orientations to learn to recognize it; it inherently understands that a rotated object is still the same object. This leads to better generalization and performance, particularly in scientific applications. === Mathematical foundation === Equivariant neural networks use principles from group theory to create operations that respect geometric symmetries, such as the SO(3) group for 3D rotations or the E(3) group for rotations and translations. Instead of learning standard filter kernels, these networks learn how to combine a fixed set of basis kernels. These basis functions are chosen so that they have well-defined behaviors under transformation groups. Spherical harmonics are frequently used as basis functions because they form a complete set of functions that behave predictably under rotation, making them ideal for creating steerable 3D kernels. Features within the network are treated as geometric tensors, which are mathematical objects (like scalars or vectors) that are "typed" by their behavior under transformations. These types correspond to the irreducible representations (irreps) of the group. The tensor product is the fundamental operation used to combine these typed features in a way that preserves equivariance, guaranteeing that the network as a whole respects the desired symmetry. Frameworks like e3nn simplify the construction of these networks by automating the complex mathematics of irreducible representations and tensor products. === Applications === Steerable and equivariant models are highly effective for problems with inherent geometric symmetries. Examples include: Protein structure analysis: SE(3)-equivariant networks can process 3D molecular structures while respecting their rotational and translational symmetries. 3D Point cloud processing: Rotation-equivariant filters built from steerable spherical functions can perform tasks like 3D shape classification. Computational chemistry: E(3)-equivariant graph neural networks are used to model interatomic potentials for molecular dynamics simulations, creating highly accurate and data-efficient models of physical systems.
Cognitive tutor
A cognitive tutor is a particular kind of intelligent tutoring system that utilizes a cognitive model to provide feedback to students as they are working through problems. This feedback will immediately inform students of the correctness, or incorrectness, of their actions in the tutor interface; however, cognitive tutors also have the ability to provide context-sensitive hints and instruction to guide students towards reasonable next steps. == Introduction == The name of Cognitive Tutor now usually refers to a particular type of intelligent tutoring system produced by Carnegie Learning for high school mathematics based on John Anderson's ACT-R theory of human cognition. However, cognitive tutors were originally developed to test ACT-R theory for research purposes since the early 1980s and they are developed also for other areas and subjects such as computer programming and science. Cognitive Tutors can be implemented into classrooms as a part of blended learning that combines textbook and software activities. The Cognitive Tutor programs utilize cognitive model and are based on model tracing and knowledge tracing. Model tracing means that the cognitive tutor checks every action performed by students such as entering a value or clicking a button, while knowledge tracing is used to calculate the required skills students learned by measuring them on a bar chart called Skillometer. Model tracing and knowledge tracing are essentially used to monitor students' learning progress, guide students to correct path to problem solving, and provide feedback. The Institute of Education Sciences published several reports regarding the effectiveness of Carnegie Cognitive Tutor. A 2013 report concluded that Carnegie Learning Curricula and Cognitive Tutor was found to have mixed effects on mathematics achievement for high school students. The report identified 27 studies that investigate the effectiveness of Cognitive Tutor, and the conclusion is based on 6 studies that meet What Works Clearinghouse standards. Among the 6 studies included, 5 of them show intermediate to significant positive effect, while 1 study shows statistically significant negative effect. Another report published by Institute of Education Sciences in 2009 found that Cognitive Tutor Algebra I to have potentially positive effects on math achievement based on only 1 study out of 14 studies that meets What Works Clearinghouse standards. It should be understood that What Works Clearinghouse standards call for relatively large numbers of participants, true random assignments to groups, and for a control group receiving either no treatment or a different treatment. Such experimental conditions are difficult to meet in schools, and thus only a small percentage of studies in education meet the standards of this clearinghouse, even though they may still be of value. == Theoretical foundations == === Four-component architecture === Intelligent tutoring systems (ITS) have a four-component architecture: a domain model, a student model, a tutoring model and an interface component. The domain model contains the rules, concepts, and knowledge related to the domain to be learned. It helps to evaluate students' performance and detect students' errors by setting a standard of domain expertise. The student model, the central component of an ITS, is expected to contain knowledge about the students: their cognitive and affective states, and their progress as they learn. The function of the student model is threefold: to gather data from and about the learner, to represent the learner's knowledge and learning process, and to perform diagnostics of a student's knowledge and select optimal pedagogical strategies. The tutoring model uses the data gained from the domain model and student model to make decisions about tutoring strategies such as whether or not to intervene, or when and how to intervene. Functions of the tutoring model include instruction delivery and content planning. The interface component reflects the decisions made by the tutoring model in different forms such as Socratic dialogs, feedback and hints. Students interact with the tutor through the learning interface, also known as communication. The interface provides domain knowledge elements. === Cognitive model === A cognitive model replicates the domain knowledge and skills comparable to that of a human expert or an advanced student of the domain. A cognitive model enables intelligent tutoring systems to respond to problem-solving situations in a way similar to a human tutor. A tutoring system adopting a cognitive model is called a cognitive tutor. A cognitive model is an expert system that generates a multitude of solutions to the problems presented to students. The cognitive model is used to trace each student's solution through complex alternative solution paths, enabling the tutor to provide step-by-step feedback and advice, and to maintain a targeted model of the student's knowledge based on student performance. === Cognitive Tutors === Cognitive Tutors provide step-by-step guidance as a learner develops a complex problem-solving skill through practice. Typically, cognitive tutors provide such forms of support as: (a) a problem-solving environment that is designed rich and "thinking visible"; (b) step-by-step feedback on student performance; (c) feedback messages specific to errors; (d) context-specific next-step hints at student's request, and (e) individualized problem selection. Cognitive Tutors accomplish two of the principal tasks characteristic of human tutoring: (1) monitors the student's performance and providing context-specific individual instruction, and (2) monitors the student's learning and selects appropriate problem-solving activities. Both cognitive model and two underlying algorithms, model tracing and knowledge tracing, are used to monitor the student's learning. In model tracing, the cognitive tutor uses the cognitive model in complex problems to follow the student's individual path and provide prompt accuracy feedback and context-specific advice. In knowledge tracing, the cognitive tutor uses a Bayesian Knowledge Tracing method of evaluating the student's knowledge and uses this student model to select appropriate problems for each student. === Cognitive architecture === Cognitive tutor development is guided by ACT-R cognitive architecture, which specifies the underlying framework developing the cognitive model or expert component of a cognitive tutor. ACT-R, a member of the ACT family, is the most recent cognitive architecture, devoted primarily to modelling human behavior. ACT-R includes a declarative memory of factual knowledge and a procedural memory of production rules. The architecture functions by matching productions on perceptions and facts, mediated by the real-valued activation levels of objects, and executing them to affect the environment or alter declarative memory. ACT-R has been used to model psychological aspects such as memory, attention, reasoning, problem solving, and language processing. == Application and utilization == The first real world applications of cognitive tutors were in the 1980s and involved a geometry proof tutor used by high school students and a LISP programming tutor used by college students in a mini course in introductory programming course at Carnegie Mellon University. Since then, cognitive tutors have been used in a variety of scenarios, with a few organizations developing their own cognitive tutor programs. These programs have been used with students spanning elementary school through university level, though primarily in the subject areas of Computer Programming, Mathematics, and Science. One of the first organizations to develop a system for use within the school system was the PACT Center at Carnegie Mellon University. Their aim was to "...develop systems that provide individualized assistance to students as they work on challenging real-world problems in complex domains such as computer programming, algebra and geometry". PACT's most successful product was the Cognitive Tutor Algebra course. Originally created in the early 1990s, this course was in use in 75 schools through the U.S. by 1999, and then its spin-off company, Carnegie Learning, now offers tutors to thousands of schools in the U.S. The Carnegie Mellon Cognitive Tutor has been shown to raise students' math test scores in high school and middle-school classrooms, and their Algebra course was designated one of five exemplary curricula for K-12 mathematics educated by the US Department of Education. There were several research projects conducted by the PACT Center to utilize Cognitive tutor for courses in Excel and to develop an intelligent tutoring system for algebra expression writing, called Ms. Lindquist. Further, in 2005, Carnegie Learning released Bridge to Algebra, a product intended for middle schools that was piloted in over 100 schools. Cognitive tutoring software is continuing to be used.