1 Introduction

Over the 20th century, the pedagogical sciences have increasingly extended questions about student success or failure beyond biology. Recent answers look to environmental conditions, including how instruction supports or undercuts learning ecologies. From pre-K classrooms to university-level departments, thinking about direct instruction or differentiating instruction, the hours of instruction or planning instruction, the quality of instruction or the pace of instruction—instruction and its strategies mediate particular outcomes (Polikoff, 2012) for learner performance, classroom achievement, and institutional effectiveness. Configuring instruction so decidedly interlocks with learning climates, student achievement, and even life outcomes that a recent Organization for Economic Co-operation and Development-based publication (Donné et al., 2016) linked instruction’s design with national prosperity. Through these correlations, and with instructional design’s increasing international influence (Bodily et al., 2019; Seel & Dijkstra, 2004), a growing consensus (Merrill et al., 1996; Piña, 2017) has emerged about the virtues of designing instruction: that the “the societal and economical”—and thus the political and ethical—“importance of instructional design seems to be relatively uncontested” (Seel et al., 2017, p. vii).

Below turns from this consensus view and contemporary discourses about instructional design to take a different approach. The article considers the epistemic principles of instructional design by exploring historically and culturally the making of instruction’s formal design as a pedagogical object. Current discussions can often treat instructional design as a pre-existing object. The central problem lurking in such research on how to design instruction is the assumption of creating strategies for more effective student achievement. The article reverses this central research problem to focus instead on how the design of instruction’s design and its study is constituted into an object of inquiry. The article aims to accomplish this by posing several questions about contemporary discourses about designing instruction. How do instructional design’s current problems become constituted as problems? What historical dimensions, conceptual foundations, and technological assumptions relate to organizing instructional design as a field of study? Might future research understand this field otherwise? Does designing instruction embody cultural values that relate to social exclusion and equity? In short, is there a politics to IDT?

Moreover, why are these questions important? Because concentrating on the epistemic principles that generate what is made intelligible when designing instruction rests on making the cultural production of designing instruction visible and its relationship to politics and governing. The purpose seeks to provide another way to think through how designing instruction can express an onto-epistemological framework that generates conceptual differences and divisions to organize what qualifies and what is to be experienced when existing traditions explain how to configure learning environments. Focusing, however, on ‘the reason’ of instruction, its epistemic principles, and what works in classrooms suggests that designing instruction exceeds just instruction. Instead, designing instruction designs human interiors, fashioning subjectivities by making up human kinds (Hacking, 2007).

The essay argues that formalizing instruction’s design operates as social technology by inscribing a sense of politics that engineers cultural action to change, move, and position people. It uses the field of Instructional Design and Technology (IDT) as the exemplar for how instruction’s formalized practices are conceptualized today. While definitions of IDT are considered shortly, below concentrates on what explanatory mechanisms within IDT’s existing traditions suggest about how instruction’s design principles work. Engineering instruction’s formalized processes design a heterogeneous assembly that scaffolds practical knowledge, reasonable assumptions, and effective actions to network users (humans), material goods (environments, equipment), and academic content (information) to operate as a system. That system, often used to produce a sense of collective belonging and social inclusion, can produce exclusion and inequality by composing relations between people, power, and technology, which implicates how humans are imagined, what politics means, and what counts as technology.

2 Theoretical background

This essay’s interdisciplinary lens explores how rules of reasoning in broader social and historical contexts design instructional design as a pedagogical object. The approach to IDT draws upon textual analysis and historical strategies, and the study uses culture, politics, and technology to explore which components assemble IDT, what powers its mechanisms, and the overall social effects. The analysis draws on cultural and critical studies of education to challenge how existing traditions in Anglo-American settings—but particularly in the United States, where IDT holds significant sway—develop the pedagogical knowledge and practical applications that IDT’s formalized models embody. This section presents the concepts and terms used below.

2.1 The political

Is there a politics to IDT? Knowledge about designing learning environments indeed carries political dimensions, and this know-how foregrounds particular reasonings and theoretical assumptions that extend beyond just ‘ideas’ about people, power, and technology. For example, questions about politics, schooling, or designing instruction are not new. Such questions, however, often frame politics as who’s rule or who’s power (Petrina, 2004), as bias or hidden curriculum (Barab et al., 2007; Brown & Green, 2020), or as state-focused politics (Larke, 2019). While these studies are needed, they reflect a traditional focus on who is the authority or what provisions wealth and privilege based on a particular theory of the individual as a causal agent of change.

The focus below, however, inverts this approach to use politics differently. One, politics entails how dividing practices—divisions, classifications, and distinctions—can diagram instruction and constitute human identities under a political way of reasoning. Two, later sections explore how political and historical knowledge formalizing instruction’s design can act on those identities to change and channel and position people. This way of using politics illustrates how knowledge expressing the design of IDT’s design differentiates and distinguishes what are and are not considered proper individual human qualities that disqualify certain participants. Again, the focus here anchors politics or disqualification not on school expulsion policies, educational inequity, or de jure segregation based on pre-given identities. Instead, by understanding the politics of (re)constructing human interiors and “the Self” (Eghigian et al., 2007), the aim here seeks to understand how knowledge brought into pedagogy from the social and human sciences designs learning experiences to design human experiences to constitute particular kinds of people.

Bloom’s (Bloom et al., 1956) near-ubiquitous Taxonomy, for example, divides thought along a hierarchical spectrum of lower-order and higher-order mental processes. Bloom’s “classification device” (p. 10) guides learners “in a specified direction” (p. 3) by comparing “which kinds of educative experiences are most efficient in producing a particular kind of behavior” (p. 23). Such taxonomic-based learning experiences ply the human interiority, making a kind of political subject by linking historically-formed knowledge, human thought, and political identity to individual, institutional, and national development. Human thought is not coupled historically with a feudal or totalitarian order. Instead, the Taxonomy knits developmental maturity (p. 34) and personal “integrity [to] the concept of the individual as a member of a democracy” (p. 41). Bloom’s Taxonomy helps constitute the democratic “kind of citizen the schools seek to develop” (p. 39). “Independence of thought and action” (p. 166) in citizens is defended “largely on social grounds: a democratic society thrives best when its citizens are able to arrive at their own [‘important and independent’ (p. 41)] decisions [‘about governmental problems and about their political future’ (p. 41)] rather than when someone in authority does the thinking for them” (p. 166). These independent “decisions require problem solving of a very high order” (p. 41), so Bloom’s Taxonomic method cultivates those higher-order thinking skills. It orients the ideas, character, and interior qualities of humans in a new direction, reforming how one experiences self-knowledge by designing the conditions of learning through institutional reform to guide social and political reform. Such reforms normalize the fitness of “independent decision-makers” (p. 41) for social and political participation, designating a viable human kind categorically differentiated from the unfit, the immature, and those other modes of being who lack integrity, those “undesirable or abnormal behaviors which are socially disapproved” (p. 13). Those kinds of people, excluded from participation, are so classified because they threaten both “the conduct of a democratic political system as well as a democratic way of life” (p. 41). Classification models like Bloom’s exclude as they include.

2.2 Defining a domain

Knowledge about designing learning environments also extends into IDT’s contested definitions (Bodily et al., 2019; Reiser & Dempsey, 2018). Historically, formalizing instruction’s design has left IDT characterized widely or narrowly as Instructional Design (ID) (Branch & Dousay, 2015), as instructional systems design (ISD) (Reiser, 2018) or development (Dick et al., 2022), or even as a “science” (Seel et al., 2017; the journal Instructional Science). Designing instructional methods has also been positioned relative to several elements that include solving problems (Cognition and Technology Group at Vanderbilt [CTVG], 1991; Doğan & Tüzün, 2022; Merrill, 2020), technology—which provides the name Instructional Design and Technology (IDT) (Reiser, 2018)—or information and communications technology (ICT) (Seel & Dijkstra, 2004; Yildirim et al., 2022), digital objects (Wiley, 2002), or distance education (e.g., American Journal of Distance Education). Despite this contested terrain, a legitimated field of study has developed. For consistency, below uses IDT (Instructional Design and Technology) but with a broader understanding of technology explained shortly.

Again, however, the focus below inverts the traditional approach. While these historical studies are not wrong, can taking names or contested constructs as the origins that characterize IDT as a field of study obscure ‘what counts’ when conceptualizing what is (not) designing instruction? This question seeks to direct attention to intersecting fields of knowledge and how rules and principles can assemble IDT as an object of inquiry. These rules operate through taken-for-granted discursive, analytical, and historical differentiations within and among various elements of people, power, and technological devices. For example, framing the field of study as Instructional Design and Technology (IDT) or Instructional Systems Design (or Development) (ISD) establishes logical distinctions among instruction, design, and technology or systems, or frames the field in relation to but not as information or communications technology (ICT). Reasoning through the primary, pre-sorted theoretical categories of people, power, and technology within a given context organizes a field’s formalized boundaries, who or what gets to play on it (practitioners? experts? machines?), and who or what gets studied (low-achieving learners? marginalized communities? ‘theory’?).

A field of study can thus emerge as a mechanism for configuring learning environments. Configuring such learning conditions, however, exceeds just instruction by upholding cultural theses of collective belonging and inclusion. For example, the “Universal Design for Learning” (UDL) model used “for Designing Learning Experiences” (CAST, 2020, p. 1, original emphasis) “help[s …] make instruction flexible and useful” to reach “all learners” (p. 2). It avoids “unintentionally exclud[ing] some of our learners from the opportunity to learn and participate” (p. 2). Referencing all learners references inclusive universals, suggesting how designing instruction for configuring learning environments can pursue social change (e.g., see The Journal of Applied Instructional Design’s special issue “Attending to Issues of Social Change through Learning Design,” Kopcha et al., 2021) while fostering an individual’s sense of belonging and participation. Instruction’s design can thus individualize as it collectivizes by aligning the various “complex process[es …which] promotes creativity […] in instruction that is both effective and appealing to students” (Branch & Dousay, 2015, p. 15) with those that serve institutionally as “a central part of strategic planning and management” (Seel et al., 2017, p. viii).

Developing techniques that explain how to achieve social goals suggests that IDT offers a technological solution to a political problem. Consequently, rather than naturalize instruction’s design as a pre-existing ‘thing,’ below first considers how historically-contingent components representing people, power, and technology help design IDT as a set of integrated processes operating in pedagogical spaces. These processes, which move across while staying independent of institutional settings and patterns, organize instructional action in certain ways but not others (always silently present). Such directed programming thus regulates individual and social change to diagram a particular theory of causation to form human-centered methods, constituting human identities, a political act. As such, IDT regulates through its epistemic principles, explored here neither in binary nor dialectical terms, and reflects design arrangements that manifest social technology.

2.3 Social technology

Social technology operates as “an ensemble of methods and strategies that inscribe principles for action and reflection” (Popkewitz, 2003; other approaches include Hitzig et al., 2019; Winner, 1980). Instruction’s design functions as social technology by operating as a rational mechanism across wide distances (space) and on different occasions (time) in response to a problem of useful ends, ordering thought to organize well-managed, efficient, and ‘total’ systems exercising power and authority over individuals. Such systems marshal human labor by situating human bodies into hierarchy-based large-scale social institutions like schools, the armed forces, or corporations, where higher-level freedoms and lower-level order conjoin, bringing uniformity and conformity across local, regional, and global scales.

However, the emphasis here rests neither on institutions nor institutional behavior but on how regulatory principles express them. IDT, for example, expresses rules of knowledge for how to configure learning ecosystems. When formalized as a ‘thing,’ IDT’s epistemic principles regulate instruction, standardize teaching and learning, and govern pedagogical action large and small. Regulating instruction regulates human identities, like the instructor—a particular kind of person who is never born yet must be made. Regarding instruction’s formalized design as social technology thus peers beyond technological or social determinism to consider how categories about humans, power, and technology entangle to regulate pedagogical change for social change.

The following section explores how traditional conceptualizations organize these categories to explain IDT’s design. Those traditional explanations about instructor and student success, however, smuggle in a central political claim, which allows reframing instruction’s design not as natural nor neutral (Oliver, 2013) but as social technology.

3 Principles of IDT

A series of epistemic principles order IDT’s design. How rules of knowledge express IDT’s traditional conceptualization can be understood by exploring how reasoning through a series of historically inscribed binaries, distinctions, and classifications orders and allocates basic theoretical categories of people, power, and technology.

3.1 Binaries

Pedagogy’s theory/practice divide provides an initial example. One, this binarism often designates particular ‘theories’ outside of ‘action’ (e.g., the Journal of Applied Instructional Design). Such distinctions often obscure the theoretical aspects of practical or useful knowledge, limiting scholarly discussions to advancing certain understandings of a field of study, an analytical move that privileges the practical application of knowledge yet overlook the epistemological dimensions of that knowledge, such as how “each component of [instructional design] model[s are] based on theory” (Dick et al., 2022, p. 3). Two, such distinctions can refuse a logic whereby categories like blackness (Thomas & Columbus, 2009) or gender (Butler, 2011) can operate materially, obscuring how power relations can generate pedagogical practices of social exclusion or marginalization.

Another binary is the subject-object duality (e.g., Fenwick & Edwards, 2010). Like Bloom’s Taxonomy, instruction’s design inscribes subjectivities, organizing a rational Western liberal agent (a subject) who utilizes separate and distinct technology (an object) to facilitate pedagogical change to alleviate a negative state (e.g., digital divides [Kim, 2018] or a cultural lag [Chen, 2007]). IDT also often theorizes technology as a thing: a physical tool, hardware (or software when coding or using social media) that can be owned and redistributed, a representation of technology re-examined shortly. A kind of person—the technology-empowered human—then enacts desired educational changes by adjusting internal or external variables (like academic content) in a pedagogical environment (Seel et al., 2017, p. 4–5; the journal Learning Environments Research).

3.2 Power dimensions

Other epistemic principles help order IDT’s traditional conceptualization. First, conceptualizing IDT embodies a particular theory of “sovereign power” (Butler, 2011; Foucault, 1977). The sovereign view of power explains causally how independent, self-determining, or “sovereign” human actors design instruction (e.g., CTVG, 1991; Hernández-Leo et al., 2019). Representing humans as power-wielding agents reflects a particular theory of the individual, where an intentional human subject ‘speaks’ or ‘acts’ or wields physical objects (technology) to change learning environments, partitioning a social world from a physical one (another binary). Moreover, this theory of (human) causation in IDT’s design embodies a politics by describing how the empowered reflective practitioner—Bloom’s mature, upstanding, independent decisionmaker—determines possible action.

Second, the sovereign view also reflects how power flows negatively (Butler, 2011; Foucault, 1977). Power is regarded as a ‘thing’ that can be owned, allocated, and exercised by autonomous, self-reflective (sovereign) humans or institutions to reach particular goal states. Those same humans or institutional structures, however, can make things go wrong. Humans can prevent classroom effectiveness or preserve exclusionary institutional structures that counteract those social goals (e.g., Kopcha et al., 2021), a negative condition that requires correction.

A series of principles and assumptions thus configure IDT’s traditional conceptualization. One such dimension is regulatory. Allocating spaces occupied by humans (subject) and technology (object) across a conceptual field pre-arranges categories of people, power, and technology, helping configure IDT as a pedagogical object of reflection that studies instructor or student success and regulates pedagogical activity around pre-figured goals. Another dimension is IDT’s central political claim. This traditional conceptualization provides a human-centered explanation that theorizes people in power to regulate pedagogy for goal states like successful learning. This focus on the rulers and ruled relies causally on particular categorical representations that locate humans and human agency in command and control yet elides how other power dimensions contribute to designing those humans or the field.

3.3 Understanding otherwise

Can reconfiguring the same categories of people, power, and technology help understand IDT’s design otherwise? This reconfiguration—posed descriptively, not normatively—brings into high relief other political dimensions and causal claims that situate not humans but technology in charge.

Discussions relating pedagogy to social goals are indeed important. Moreover, this part draws on yet extends those discussions to consider other categorical representations of people, power, and technology for the next section’s exploration of IDT’s formalized models. That section considers IDT’s entire technological ecosystem, the basis of its models in rule-based programming and control technology, and historically problematizes IDT’s central political claim about human agency. Understanding IDT’s internal organization (explored heretofore) and external justifications (explored later) illuminates how a series of components design instruction as social technology that can design exclusion and inequality.

This part, however, begins by exploring how complementary representations of power and technology can re-vision traditional understandings of IDT. First, consider productive power (Butler, 2011; Foucault, 1977). Recall how IDT’s negative view theorizes a sovereign subject wielding power as an object or ‘thing,’ recapitulating how the subject-object duality diagrams different spaces on a conceptual field occupied by humans (subject) or technology (object). Productive power’s positive view reconsiders this order to reconsider the flow of power out of human hands. This productive view regards the subject-object duality and its negative flow of power already as objects of power and technology, for people, power, and technology already entangle in an earlier conceptual field before IDT theorizes the empowered (human) subject wielding device (object) technology. This complementary view takes the traditional understanding of instructional design and technology from being a fundamentally human-driven phenomenon centered in consciousness and social processes (Stefaniak & Reese, 2022; Wiley, 2002) to one in which both power and technology constitute those human and social processes. IDT’s traditional ‘negative’ theory of power focuses on the empowered human agent controlling and improving technology (devices) to control and improve the human condition. Using productive power, however, ‘flips the script’ to understand how the human agent is produced as an object of technology, already looped in and encoded by technology’s pre-ordained conceptual spaces.

A second categorical representation reconsiders that notoriously vague word technology. Again, a particular theory of technology—object-centered technology, technology as a tool, machinery, or a material thing (including software) wielded by a human subject—organizes IDT’s traditional conceptualization. However, peering beyond this ‘device’ orientation reconsiders technology as know-how, as a methodology, since technology-as-methodology constructs physical objects into technological devices that control the world (Marx, 2010; Schatzberg, 2018). A knowing technique (techno-logy) is equally important as the physical materials gathered to organize and produce what many consider object-centered technology. While scholarship can entertain this broader technological conception concerning IDT (Oliver, 2013; Reiser, 2018), focusing on knowing techniques focuses on technology’s antecedent and productive conceptual spaces.

How do a knowing technique view of technology and its productive dimensions make social technology? Knowing techniques and organizational methods (such as programming formal rules—algorithms—discussed later) operate in earlier spaces to later assemble parts into wholes to make things work. For example, scattered pebbles or beads remain so until a technique knows how to organize them into an abacus, a calculating machine, a technological device central to the traditional representation of object technology wielded by a subject. Likewise, coupling technology’s antecedent conceptual spaces with later material substances under a single frame of analysis allows assembling various physical systems like computers. The linchpin is how that same coupling capacity also enables knowing how to assemble various human organizations like social systems: social technologies that couple human bodies with modes of existence. Such conceptual models of social organization harness human labor to design stable political-economic systems such as worker collectives, intentional communities (communes), or laissez-faire capitalism. Folding humans into information ecologies, where technology like information cascades or feedback (Vaquero et al., 2019) passes among material objects like walls, complex machines, and humans alike, becomes possible because such technology reflects a productive view of power and knowledge. That knowing technique does not later diagram spaces bifurcating humans (subject) and technology (object). Instead, IDT’s collectivizing techniques have already situated information processing devices and humans in the same space on the same conceptual field.

Another technology-related point (and binary that helps IDT work) is object technology’s digital properties. Analog technology operates within contexts dependent on a physical medium (e.g., wired technology) to channel real-time (continuous) fluctuating signals (or data). Digital technology, however, decontextualizes such processes, operating unconstrained by physical media (e.g., wireless devices) to channel information ‘on-demand’ (discontinuous time) for ‘distance,’ ‘virtual,’ or ‘cyber’ learning (discontinuous physical spaces). For example, the promise of communicating academic content through something like a MOOC, focuses on the digital reproduction of orienting homologous learning and behavior outcomes across different spatiotemporal contexts among different people (Bayne et al., 2014). This action does not straightforwardly erase or neutralize but reshapes the dimensions of various spatiotemporal contexts. With an eye to the future, such action centralizes legitimate, reasonable conduct for the autonomous, flexible, planned, and effective reproduction of outcomes within a locally defined context. When digital reproduction widely distributes a standardized design for programming instruction (Piña, 2017), the same focus holds for configuring learning and behavior outcomes across institutional settings. Again, IDT’s traditional conceptualization theorizing (object) technology in many ways already couples knowing techniques and human bodies.

Describing these various categorical representations relating people, power, and technology helps provide one basis for reframing instruction’s formalized design. The traditional conceptualization designing IDT’s internal design relies on a series of entangled practices, theories, and categories dividing people, power, and technology to diagram an explanatory mechanism for how to configure instruction for student success. That conceptualization carries a central political claim about human agency.

The following section, however, considers historically how a different entanglement of people, power, and technological know-how troubles that political claim. It explores how the practical ‘theory’ and useful knowledge when IDT’s formalized models design instruction resides in information and communications theory. That technology (a knowing technique) forms the algorithms that engineer instruction as social technology, producing a “Looping Effect” (Hacking, 1995) that repositions the research, the researcher, and the researched alongside device technology within the same conceptual field.

4 IDT models

Approaching technology’s productive spaces helps make intelligible how productive and negative power flow within the architectural design of instruction’s formalized models. This architecture (e.g., Gibbons, 2013), common among IDT models, help them operate as social technology by expressing overlooked political strategies that regulate human locomotion in a social field.

For example, widely available instructional models include the “Tyler Rationale,” Understanding by Design (UbD), ADDIE, or Anchored Instruction. Each model forms an architecture by organizing educational programming under a sequence of action steps across a time series. Ralph Tyler’s (2013/1949) “Rationale” or Basic Principles of Curriculum and Instruction, designs instruction by guiding people through four questions or steps:

  1. (1)

    What educational purposes should the school seek?

  2. (2)

    What educational experiences can be provided to attain these purposes?

  3. (3)

    How can these educational experiences be effectively organized?

  4. (4)

    How can we determine whether these purposes are being attained?

Step One sets a desired educational goal state. Steps Two and Three configure learning experiences around that goal, and evaluating the goal’s achievement (Step Four) closes this model. The UbD framework (Wiggins & McTighe, 2005) draws on Tyler’s Rationale (pp. 20, 298, 338) to present a three-stage “backwards planning” model. Stage 1 calls for “Identify[ing] Desired Results” (McTighe & Wiggins, 2012, p. 2) of learning by identifying the intended goal state. Stage 2’s “backwards” planning move jumps ahead, creating the proper mechanisms that evaluate the “desired results identified in Stage 1” (p. 5), yet only implements them in Stage 3 when instructors “plan the most appropriate lessons and learning activities to address the […] goals identified in Stage 1” (p. 6). Note that “A key component of [… the] UbD framework is alignment […] all three stages must clearly align” (p. 2). Another model, ADDIE (“Analysis, Design, Development, Implementation, Evaluation”), developed under educational psychologist Robert Gagné’s oversight (Branson & Others, 1977), offers five steps or “phases.” Beginning with a needs Analysis—needs serve as goal states or inputs—the next steps articulate the Design, Development, and Implement phases of instruction. Evaluation closes the process (Branch, 2009). Finally, the Cognition and Technology Group at Vanderbilt (CTGV, 1991) based Anchored Instruction’s general “problem-solving approach” in the contexts of situated cognition and instructional (object-oriented) technology. Anchored Instruction follows the five phases or steps of Bransford and Stein’s ([1984] 1993) “IDEAL Problem Solver” model. The IDEAL steps first Identify the problem, Define the goal, Explore the goal, Act on the plan, and finally, Look back to evaluate the achievements. Anchored Instruction’s strategy of flexible action implements IDEAL’s phases—highlighting a critical feature to remember—by mapping “decision points” (McLarty et al., 1990, p. 112) based on that “most important decision” (p. 113), the first step’s “clearly articulated set of instructional goals.”

Historically exploring these various instructional models (for more, see Branch & Dousay, 2015; Seel et al., 2017) shows how common procedures articulate the same formalized theoretical construct. Absent any linear stepwise rigid order, each model determines needs and goals, implements an instruction set to reach them, and concludes with evaluation. Organizing standard procedures collectively forms a common architecture. This common architecture functions as a rational mechanism for designing effective instruction by organizing learning ecologies that channel people through technical and physical arrangements. This technological design acts on instructor and student.

4.1 Communications and control

Before entertaining algorithms, this common architecture’s communications, cultural, and political properties warrant discussion. First, information and communications theory guide the practical and applied knowledge these models embody. Conceptually designing instruction’s familiar framework engineers a dynamic communications system (cybernetics). One, taken individually, each step within each stand-alone model marks a specific event, a state or condition and its consequences operating in a particular space at a particular time (e.g., Gagné’s Events of Instruction) carrying no particular connection to another past or future event. Together, however, linking disparate events alongside physical materials and other pedagogical practices generates instruction as a set of events to facilitate learning (Seel et al., 2017, Ch. 2; Wiley, 2002, p. 6).

Two, linking such disparate events assembles—and highlights another noteworthy critical feature—a circuit, reflecting a self-contained communications system. Each stand-alone model engineers a goal state’s launching point that reaches through a set of intermediate steps to an endpoint that evaluates the outcomes of pedagogical action. The ecological totality of these accumulated events—including those not considered—constitutes communications with the learner (Gagné et al., 2005; Richey et al., 2011, Ch 3). Such communications express a regulatory pattern—a message—by positioning human subjects, environmental conditions, and material objects within the same domain, where “printed text, […] a TV program, an instructor’s talking, [and] other physical means, are all considered media” (Gagné et al., 1992, p. 208, emphasis added; also Reiser, 2018, pp. 8–9; Richey et al., 2011, pp. 93–94).

Three: at a higher level of generalization, theorizing how to engineer a communications system hinges on the mid-20th century science of cybernetics (Wiener, 1948) that erases conceptual borders separating categories of “man” from machine (see Gagné’s [1962] “man-machine systems”; Tyler’s “thinking machine” [Cooperative Study in General Education, 1947, pp. 65–70] is reproduced in Tyler, 2013/1949, p. 50; also Hayles, 1999), which cognitive psychology still embodies (Dupuy, 2009; Newell, 1980). This cybernetic view suggests processes that exceed just instruction. Recontextualizing instruction’s design as communications technology allows reframing instruction’s design into what Wiener’s (2019/1961, p. xlv) Cybernetics called the “programming of programming,” the architecture of which embodies cybernetic and other systems principles (Gibbons, 2013; Seel et al., 2017). Rather than bifurcating human subjects and object technology under a sovereign power model, cybernetic knowledge locates both within the same conceptual domain to compose a particular human amenable to communications exchange with other information technology.

Second, as IDT’s common architecture operates on humans in a universal, large-scale, decentralized fashion, other digital, cultural, and political-economic qualities emerge. One, such models can act digitally. Instruction’s formalized design can operate universally, transmitting discontinuous signals (academic content, data) that transcend spatiotemporal classifications to form real-time, anytime/anywhere learning ecologies.

Two, the cultural properties of such models enable large-scale institutional programming. The properties transcend educational, social, or cultural values (Heineke & McTighe, 2018) across institutions, geographies (rural, urban, suburban), economics (private, public, or varying funding levels), academics (primary, secondary, postsecondary), or even learning environments (face-to-face, hybrid, or online). Such models instruct learners the same, whether at the individual, small-group, or large-group instructional level (like a MOOC), functioning with no particular academic content because their design works with all possible academic content. From mathematics to learning languages, this communications technology informs best instructional practices. Three, such models function politically. As each step indicates a preferred choice, each model helps inexperienced instructors acquire independent action under decentralized control. Such pedagogical planning models direct action at a distance, coordinating the same functional human labor across time and space without external policing. This form of democratic action optimizes organizational cultures, reduces economic inefficiencies, and secures future institutional settings by minimizing trial and error and waste among educational systems distributed across local, regional, and (increasingly) international contexts.

These properties suggest how formalizing instruction’s design acts as a political mechanism, a technological solution to a social problem. IDT’s programming architecture economizes a society’s educational system by planning standard effective procedures. In this way, the model’s ensemble of reason, strategies, and methods exercises power and authority. This ensemble couples conceptual ways of being with physical bodies to coordinate human labor among political-economic structures according to stabilized social ends. Aggregating these political moves troubles instructional design’s causal claims by positioning “knowing techniques” (techno-logy) in control, not humans, fostering instruction’s design as social technology.

4.2 Algorithmic reasoning: programming instructional landscapes

Beyond the communications, cultural, and political properties, how does productive power design the common architecture among instruction’s formalized models from which negative power can flow? By mapping. “Classroom teachers usually view any ID[T] model as a general road map” (Branch, 2009, p. 41; also, Cennamo & Kalk, 2019; Wiggins & McTighe, 2005) and the common architecture that configures instruction “may be represented as a kind of map of the terrain to be covered in progressing from one point in human development to any other” (Gagné et al., 1992, p. 74, original emphasis). Productive power generates both this cultural terrain and the algorithms that enable each model’s cartographic capacities to reform any outdated classroom instructional ideas into new configurations. This section spells out instructional design’s algorithmic reasoning and then how the design organizes mapping, a cultural territory over physical spaces. The following considers human subjects in those spaces.

To begin, the aggregated steps of each self-contained instructional system program an algorithm to achieve effective instruction. Just as earlier sections extended the traditional understanding of technology, understanding algorithms as technology also extends beyond a string of characters and numbers as computer code. Algorithms, as rule-based programming, work within human decision-making to provide “any effective procedure that reduces the solution of a problem to a predetermined sequence of actions” (Nowviskie, 2014, p. 1; Chabert & Barbin, 1999). Instruction, as a rule-based programming procedure, deliberately arranges sets of exterior events to stimulate internal learning processes (McNeill & Fitch, 2022; Hernández-Leo et al., 2019), which summons the two critical features referenced earlier: linking disparate decision points to assemble instruction’s action steps as a circuit makes an algorithm (focusing on algorithmic reasoning considers broadly how an algorithm and the more intuitive heuristic together provide the same rule-based problem-solving strategy [e.g., (Gigerenzer, 2022, Ch. 2; Gigerenzer et al. 2000;).

The productive power latent in each self-contained instructional system’s algorithmic strategy is cartographic because each algorithm begins nowhere. What exists before each formalized model’s first possible action step? Nothing. Simply empty space. This dynamic originating field is barren of people or things, lined only with multiple paths of (im)possibilities and alternative moves for realizing effective instruction. Any possible first step requires a choice of selecting a decision point. That decision must accommodate dynamic conditions, weigh various probabilities of success, and reconcile the benefits and risks associated with each alternative move. The outcome of each initial (and subsequent) decision at one moment in time from each position on that originating field generates an event that provides access to particular states or conditions within an educational milieu. Various instructional models chart various rules of choice, and each instructional model’s strategic calculus guides instruction—and thus steers instructor, student, and object technology—to a future goal state by mapping sequential actions across a time series. Moreover, as with any decision tree or a move on a chessboard, path dependency exists: any ‘practical’ alternative action on that field is already pruned by a preceding one, and any payoff resulting from one state or condition may be sacrificed in another (Seel et al., 2017, p. 11). Nonetheless, the programming of instructional programming housed within each formalized model projects an aggregated set of behavior rules of planned action steps through a now-formed educational terrain. The familiar architecture among formalized models reflects a well-ordered point-to-point decision path across a field, divorced from meaning, indistinguishable from machine programming, yet generates future performance regarded as effective instruction.

Another consensus thus emerges around formalizing instruction: when communicating academic content, effective instruction comes by developing a system of correct action. That action programs a best route—a map—to produce desired educational conditions by engineering a system of instruction and learning in a complex information-based ecosystem.

To review, productive and negative power interact to constitute instruction’s design as media and as social technology that facilitates particular human activity towards a goal state by moving people across certain channels and not others. Formalized models operate as a knowing technique (technology) that culturally colonizes pedagogical spaces for action. Communications engineering and programming language provide the theoretic aspects of useful knowledge to code algorithms that enable instruction’s common architecture to organize more stable, more predictable, and less challenging learning ecologies. When designing instruction, this overarching ‘ecological’ focus allows deliberating the entire set of possible conditions or effects of various programming decisions. However, beyond prevailing ecological priorities, designing various models also reproduces the same transcendent action steps to ply the human interiority by coordinating outside factors to reproduce learning goals inside a learner. Such practices derive from productive power to organize human thought, complex machines, and information flows on the same originating field. Human labor, devices, and institutional policies then align to secure coherent, focused, and harmonious future social goals, each decontextualized from particular historical, cultural, or social instances and each organized for well-regulated, large-scale, and planned group action. Programming instruction’s programming thus produces its objects of reflection. Just not the existing tradition’s causal claim about the sovereign, intentional human using today’s latest greatest gadget. Instead, under conditions where technology ‘speaks’ first, the posthuman is formed (Hayles, 1999). The following section explores how these posthuman identities of the instructor and instructed—and inequality—are constituted.

4.3 Configuring human relations

Formalizing instruction’s common algorithmic methodology produces the instructor and instructed, each positioned across hierarchized territorialized social spaces while administering human dispositions under a looping effect. First, since instructional “events are communications external to the learner that support the internal processes of learning” (Gagné et al., 2005, p. 194; also, McNeill & Fitch, 2022), configuring external learning conditions configures the learner’s internal conditions. Structuring external events author-izes internal learning, whereby a stable, well-controlled learning ecology constitutes the order, coherence, and conditions of how instructor and student experience the world. Again, at center stage sits cybernetic principles, the greater system of engineering communications that interface humans with algorithms, self-regulating machines, information flows, and data retrieval. Representing humans in a particular way upstream—as information processing systems—enables human-machine communications downstream, upending political claims in traditional explanations.

Second, designing instruction’s cartographic strategies politically positions people across hierarchical, unequal social spaces. The central unit of analysis within instruction’s common architecture focuses on the decision, not the decider (the instructor) (e.g., Dick et al., 2022, Chs. 9, 11; Hernández-Leo et al., 2019; Stefaniak, 2021). As with Anchored Instruction’s focus on decision points, this transcendent ecological vantage point accommodates various freedoms to ‘see’ numerous factors on a given social field: problems in the current state of learning; forecasting learning outcomes from each possible short-term instructional path; predicting which long-term instructional paths are most effective for a desired future state. Since an earlier decision controls later action—that is, information eclipses energy—this ‘upward’ hierarchical gaze embodies the capacity for expertise to exert itself. Algorithmic strategies accentuate an instructional programmer’s computational thinking by guiding each to the best choice when designing an effective instructional strategy for flexible action, structuring external conditions to regulate large-scale educational (im)possibilities. This (social) control technology responds to a pedagogical problem that ‘fixes’ a learner’s internal state by always already knowing what an effective table of instructions entails. Instruction’s methods thus loop back to adulterate the expert’s subjective experiences, troubling the assumed neutrality (Dick et al., 2022, p. 4) of instructional models and expertise.

Designing instruction can also appear as a common ‘good’ by positioning and orienting lower-level subjectivities by acting ‘downward.’ Since “learning [consists] of getting the student from one state of mind to another” (Gagné et al., 1992, p. 186), instruction’s algorithms hew desired states of mind, arranging particular external events or conditions, yet not others, to produce particular kinds of learners, yet not others. Who, for example, is the particular human type constructed as the problem solver? In Anchored Instruction, the IDEAL problem solver is a category of the individual separated as different. Such a person is never born but must be made. Consequently, Anchored Instruction’s algorithmic processes seek collective belonging and inclusion by configuring a learner’s internal qualities for “effective problem-solving, reasoning, and learning skills” to address “the failures of our schools and our society” (CTGV, 1991, p. 34), yet excludes by acting on those operating outside of reason and practical problem-solving, correcting those system ‘errors’ who threaten social stability and consensus. Engineering well-controlled external conditions works on human consciousness to improve deficiencies in learner attitudes (Brown & Green, 2020; Dick et al., 2022). The politics of designing instruction thus involves research to stabilize human relations, promoting collaboration (Piña, 2017) among heterogeneous lower-level groups to foster cooperation for social learning (Shum & Ferguson, 2012) among horizontal (part-to-part) and vertical (part-to-whole) relations that subordinates now-lower-tier groups to higher-level instructions (Seel et al., 2017, Ch. 4). These edu-political research strategies then stabilize patterns relating part and whole to create docile, quiescent lower-level populations of learners through social sorting techniques that discipline and exclude those whose qualities impede notions of belonging—the at-risk (Hanghøj et al., 2018), troubled individuals (Šorgo et al., 2017), or others understood as requiring improved moral and psychosocial development (Koh, 2014)—marginalizing each from participation in greater decision-making.

5 Discussion

This investigation considered the making of instruction’s formal design as a pedagogical object. The central problem confronted the historical, conceptual, and technological dimensions of designing instructional design. Using a strategy that explored how contemporary discourse can frame and constitute ITD’s problems as a form of cultural production, the analysis inquired whether future research might understand the field otherwise by considering how designing instruction embodies cultural values connected to social exclusion and equity. It asked, in short, whether there is a politics to IDT.

Designing instruction as a system indeed embodies cultural and political values. The methodological uniformity within the common architecture (Gibbons, 2013) among IDT models reinforces a culture of acceptable social positionality, integrating human subjectivities that occupy organized, diversified, distributed, yet unequal social spaces for stabilizing well-regulated hierarchical social systems. The systems-level ecological focus determines lower-level functionality by tying lower-level behavior to the future performance of the whole, a political move that simultaneously problematizes the current state of people by bringing a new, more appropriate frame of fitness that marks a form of governance—decentralized control acting on people from a distance. This focus also erases cultural differences under anywhere/anytime parameters (Bayne et al., 2014) that provide a seemingly gender-free, sectarian-neutral, and culturally blind policy (Heineke & McTighe, 2018). Decontextualized from historical and social inequities, designing instruction’s processes thus appears to smooth over deep social rifts to mediate human relations (Shum & Ferguson, 2012; Yildirim et al., 2022) by forging an ostensible consensus (Merrill et al., 1996; Piña, 2017; Seel et al., 2017) that brings a sense of system-wide unity with notions of collective belonging (CAST, 2020). That culturally common reference point positions how interchangeable learners can acquire knowledge in the same way across various institutional learning ecologies.

One can also better ‘see’ instruction’s design as social technology, as a form of cultural action. The programming of programming instruction functions as a mechanism designed for ‘large-scale systems’ in response to a problem of social ends. Its decentralized, universal, technical arrangements already exercise power over humans, providing an organizational scheme that rationally coordinates outcomes, learning, and assessment. Its everywhere/anywhere properties improve communications and efficiencies that provide social institutions a principle of reversibility—UbD’s alignment—consonant with other aligned and centralized human activity. This cultural action conditions how educators and researchers think, see, and act when adjusting ecological factors or the internal flow of academic content to achieve social goals, not by repressing humans but by steering and regulating their locomotion to regulate social change.

These findings suggest avenues for potential future research. Future scholarship within instructional design can consider interrogating the co-extensive and co-productive relations among its assumptions about people, power, and technology, and how other such categories used in designing instruction conceal as they reveal. Such categories, for example, are often pulled into teaching and learning from the (social, life, or physical) sciences when privileging explanatory mechanisms for “what works” in classrooms. However, doing so often entails looking past those sciences’ changing historical contexts, concepts, and frameworks, such as the postwar consequences of using information theory, cybernetics, and communications in teaching and learning. Future scholarship can also explore how those assumptions and categories overlap with the social goals of inclusion and equity embedded in IDT, for inequity and exclusion are experienced worldwide. How are those unequal, exclusionary experiences constructed, however? Planned inequality can be seen as an effect of a universal blueprint (architecture) for “what works” in teaching and learning. Moreover, despite the best of intentions, the increasing international spread of instruction’s design models raises questions about equity and social justice across the Global North and carries implications for colonialism and decolonialism in the Global South. This final point entails extending questions to consider different approaches to the historical and cultural dimensions of “what works” in pedagogy by considering the non-state-based political dimensions in instructional design’s design.

This analysis of the design of instructional design turned away from the consensus view to rethink the ‘tools’ of designing instruction and its practices, particularly as the field makes international inroads. The key findings suggest how the post-war efforts to reform instruction organize ways of thinking and seeing and being by using communications and information theory to entangle humans, technology, and power. Engineering learning environments regulates conceptions of humans to map livable spaces to maintain social cohesion, operating as social technology under a political rationality. Such political properties—challenging to perceive, considering how forms of power and control can be naturalized—suggest how theory, concepts, and categories can act as an agent that ‘speaks’ in forming a field of study. Recognizing such hidden properties opens other ways to consider how undesigning instruction might produce other social and pedagogical realities.

This previous point, however, also suggests several entangled limitations of the above study in terms of understanding human participation, agency, and autonomy. One limitation involves a historical assumption: that instruction’s postwar design rests on communications. This study also assumes that the scientific-based discourses of communications embody the sense of control and governance discussed earlier (e.g., cybernetics and its governing systems). Given these assumptions, this study has not escaped the same conceptual problems of control and governance. No guarantees of eliminating power dynamics exist by asking about or analyzing instruction’s design otherwise. Another limitation is how this study should not be considered “objective” in an absolute sense. Instead, this study helped make visible the power dynamics of design by using the same language-based processes of categorization and distinctions, which, much like the communications-based scientific discourses and instructional design’s circuit, enters into a self-determined and self-referencing world, thereby already limiting the possibilities of thinking otherwise. Finally, while this study engaged in a strategy to explore the cultural politics of instructional design’s research practices, constituting this strategy meant accepting many of the same concepts, categories, and representations of instructional design for their analysis. Using an analytical move that borrowed the same terminology risks normalizing the same objects of reflection and conceptual distinctions that ground the design of instruction’s design. Adopting the same language can raise further difficulties when reconsidering instruction’s design and its related power dynamics, for that move can potentially reinstall the same ways of reasoning. Such a move does not overcome such reasonings or power dynamics. Instead, it completes them. Although these dangers are inescapable, this study seeks a de-reification of the binaries that the design of instruction tends to inscribe and questions the self-privileging of systems thinking to open doors to enclosed possibilities.

Nevertheless, exploring instruction’s post-war reforms as cultural action elicits one final political aspect: its underlying modernization project. The centuries-long Enlightenment-era project to realize social goals of belonging and inclusion by modernizing individuals and institutions has required reorganizing forms of knowledge and power relations. Contrasted against practices observed in ‘traditional’ communities (Gilman, 2003), modernization engages notions of linear historical progress and levels of material development to (re-)organize overlapping political-economic bureaucracies, social structures, and associated social technologies to articulate the proper (“modern”) cultural conditions that link individual, institutional, and social development.

Designing modern conditions, however, also requires designing a functional inhabitant whose conduct follows the anterior logic of the initial design. Instructional design’s knowledge presumes (non-random) paths in a (non-static) dynamic universe that orients how individuals see themselves, not as passive, but as active agents amenable to change, self-adjusting to dynamic circumstances according to effective procedures, decision paths, and external guidelines offered from a distance. Designing instruction as a social technology serves to administer ‘modern’ internal qualities that ‘emancipate’ human agents from external policing by democratizing people’s dispositions through practices governing the self (and others), embodying notions of social control.

Such modernization principles consequently express a culture of human improvement, a greater project that closes a perceived cultural lag between advanced technology and lesser-developed humans. However, bringing technology (techno-logy) to humans does not seek to improve the technology. It instead seeks to improve the human. Designing instruction operates on free people in particular social and cultural spaces, problematizing who they are and where they are in a social field. Changing human subjects to a more suitable station organizes personal virtues around more rational choices, away from more deficient, maladjusted, and basic animal-like instincts to normalize a kind of person who can order instruction through algorithmic reasoning while excluding those who cannot (or do not want to). Telling people who they are and are not, and where they are and where they are not, communicates a message of just who and where they should be, putting people in their place. Formalizing instruction’s design modernizes social institutions as cultural reform, administering human relations for their regulation and democratic management under a greater political project and global technological order. Before accepting instructional design’s systematic reforms, one might ask who and what have already been encoded in and out of its system.