Introduction

Imagine situations such as the following: High school students in a chemistry class visit an out-of-school lab on a university campus, where they independently conduct hands-on experiments while wearing white coats, gloves, and goggles. In a biology course at school, students present their findings on plastic waste found together with an expert in a river in their hometown. For their school history project, a group of students visits a memorial site that offers virtual reality applications to travel back in time and talk to people who lived, worked, or were forced to be at the site in the past. These are the situations we are concerned with in this paper, situations in which learners do something like people in the real world (e.g., scientists), learn something closely related to their personal real world (e.g., plastic waste in the rivers of their hometown), experience something in a supposedly real world (e.g., a historical event in the past), or learn something while being at certain real-world locations outside of school (e.g., laboratory, university, memorial site). All of these situations can be characterized as authentic learning experiences, as they try to mimic or include aspects of the real world to a certain extent.

These different examples reflect the broad use of the term authentic learning and the various perspectives on authenticity in the field of educational research. A recent synthesis of seminal conceptualizations of authentic learning found that four intentions of authentic learning and eight design elements to create for authenticity can be distinguished (Nachtigall et al., 2022). According to this synthesis, the purposes of authentic learning can refer to (1) creating personally meaningful learning activities, (2) emulating the work of professionals of a certain discipline, (3) connecting learners with a community of practitioners, and (4) reflecting experiences from real/daily life. Authentic learning settings may be characterized by one or more of these intentions, although Shaffer and Resnick (1999) call for incorporating several intentions to design for a thickly authentic learning setting. To achieve one or more of these intentions of authentic learning, the literature describes the following main design elements that can either be used in isolation or in different combinations: collaboration, complex problems/tasks, inquiry/investigation, experts/practitioners, real-life/professional setting, real-life materials/cultural tools, authentic assessment, and technology (Nachtigall et al., 2022). Certain elements, such as the setting and the materials, contribute more strongly to the physical realism (see Herrington et al. 2003) of the learning environment, which is also referred to as structural fidelity or physical resemblance (see Hamstra et al., 2014). In contrast, other elements focus more strongly on designing for the cognitive realism (see Herrington et al., 2003) or functional fidelity (see Hamstra et al., 2014) of the learning activity by, for instance, asking learners to investigate a complex real-world problem.

The different views on authenticity that are represented by the papers in the special issue “Perspectives on Authentic Learning” mostly align with one or more of the four intentions of authentic learning introduced above. Most of the papers investigate the effectiveness of authentic learning settings that intend to emulate the work of professionals. These studies either investigate learning in out-of-school labs (Hagenkötter et al., 2024; Hohrath et al. 2024; Nachtigall & Firstein, 2023) or learning with simulations (Bichler et al. 2024, Corves & M. Fischer, 2024; Stürmer et al. 2024). Out-of-school labs are non-formal learning settings that are usually visited by whole classes for a one-day project. These labs are considered to be authentic learning settings as they provide learners with authentic insights into science and research by engaging them in activities that emulate processes of scientific inquiry (e.g., hands-on experimentation), take place at research-related locations (e.g., university campus), and are often supervised by scientists (Stamer et al., 2020, 2021; Yonai et al., 2022). Thus, out-of-school labs usually try to situate the students into the role of scientists (Glowinski & Bayrhuber, 2011). Simulation-based learning also tries to situate the learners into the role of professionals. It is defined as “a technique for practice and learning […] to replace and amplify real experiences with guided ones […] that evoke or replicate substantial aspects of the real world in a fully interactive fashion” (Lateef, 2010, p. 348). Simulations provide (often in technology-supported environments) opportunities for approximation of practice (see Chernikova et al., 2020) and, thus, opportunities for learners “to engage in practices that are more or less proximal to the practices of a profession” (Grossman et al., 2009, p. 2058). In the studies of the special issue, the simulations asked medical students (see Corves & Fischer, 2024) or pre-service teachers (see Bichler et al., 2024 and Stürmer et al., 2024) to engage in activities proximal to their future practices as physicians or teachers.

The study by Güth and van Vorst (2024)investigates a context-based learning environment that focuses on reflecting experiences from everyday life with chemistry learning tasks that are contextualized with situations from the real world. Context-based learning approaches aim to “connect science concepts and principles of investigation to the students’ real life” (Menthe & Parchmann, 2015, p. 52) by using the applications and contexts of science concepts and scientific practices as the starting point for developing and learning the target scientific ideas in class (Bennet et al., 2007). In addition to such a contextualization of the tasks, Güth and van Vorst investigate the role that students’ choice of the context of the task plays for their learning. Thereby, this study is also concerned with authentic learning in the sense of creating learning activities that are personally meaningful to the learners. The study by Rahmian et al. (2024) also focuses on a view of authenticity that emphasizes the alignment between the learning activities and the personal interests of the learners. If these settings additionally align with discipline-specific practices and thus emulate the work of professionals, Rahmian et al. (2024) consider these as “doubly authentic.” Two further studies focus on the intention of reflecting real-life experiences in a particular way by bringing the learners to a historical site and thus to a real-world location of the past (see Ries & Schwan, 2023) or by focusing on realistically representing the human heart with the help of an immersive technology (see Moser & Lewalter, 2024).

As these different perspectives on authentic learning illustrate, the goal of authentic learning is to demonstrate the real-world meaning, relevance, and functionality of the to-be-learned knowledge to the learner by connecting the learning activities to the world outside the classroom to a certain extent. Thus, authentic learning settings attempt to contextualize the to-be-learned knowledge within situations and phenomena of the real world where this knowledge is needed and applied in order to make the acquisition of this knowledge meaningful to the learners (e.g., Hung et al., 2012; Lepper, 1988; Newmann & Wehlage, 1993). It is assumed that authenticity, thereby, promotes both the acquisition of knowledge that does not remain inert but can be transferred to and actually applied in different contexts (e.g., Brown et al., 1989; Renkl et al., 1996) as well as learners’ intrinsic motivation to engage with the learning content (Lepper, 1988; Newmann & Wehlage, 1993). In their literature-based model of authentic learning, Betz et al. (2016) list many more possible motivational and cognitive learning outcomes. The findings of a literature review of 50 studies on the effects of authentic learning indeed show that the majority of the reviewed (quasi-)experimental studies revealed moderate to large motivational and cognitive effects (Nachtigall et al., 2022). However, the findings of the review also suggest that authentic learning settings seem to have differential effects on motivational and cognitive learning outcomes (Nachtigall et al., 2022).

In this conceptual paper, we focus on these differential effects of authentic learning. For this purpose, we adopt a broad view on motivational and cognitive learning outcomes. Under the umbrella term of motivational outcomes, we refer to different perceptions, feelings, emotions, or internal states more generally that arise during learning and that influence learners’ motivation to (re-)engage with certain learning content and activities. This definition resembles Kraiger’s et al. (1993) definition of affectively based learning outcomes (“internal state that affects behavior”, p. 318) and Wigfield’s and Cambria’s (2010) definition of motivation-related constructs (“purposes or reasons individuals have for doing different activities and their interest in them”, p. 2). Under the umbrella term of cognitive outcomes, we subsume different types of knowledge and skills that learners acquire or develop during learning. Thereby, we subsume the cognitive (i.e., variables that relate to the acquisition, organization, and application of different types of knowledge) and skill-based (i.e., variables that relate to the acquisition, compilation, and automatization of technical or motor skills) outcomes that Kraiger et al. (1993)—based on Gagné’s (1984) categories of learning outcomes—distinguish.

To discuss the differential effects of authentic learning, we refer to empirical evidence from previous studies on authentic learning and on fields related to authentic learning, some of which are also represented in this special issue on perspectives on authentic learning, such as context-based learning, immersive learning, simulation-based learning, or learning in out-of-school labs. Thus, we have selected studies from different areas of research on or related to authentic learning that have a “complementary value” (Jaakkola, 2020, p. 19) in conceptualizing what we call the authenticity dilemma and thus in deriving theoretical assumptions about the differential effects of authentic learning on motivational and cognitive outcomes. As we aim to develop theoretical assumptions that explain and predict the relationships between authenticity in learning settings on the one hand and motivational and cognitive learning outcomes on the other hand, the present conceptual paper can be described as a model paper (see Jaakkola, 2020).

In the following sections, we (1) outline the authenticity dilemma, (2) discuss opportunities for reconceptualizing it, and (3) derive implications related to the conditions and effects of authentic learning. Finally, we discuss the findings of the studies included in this special issue through the lens of the authenticity dilemma. Across these sections, we provide different illustrations of models that sketch the authenticity dilemma (see Figs. 1 and 2), depict an opportunity to reconceptualize the authenticity dilemma (see Fig. 3), or visualize the derived assumptions about the conditions and effects of authentic learning (see Fig. 4).

Fig. 1
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One-dimensional illustration of the authenticity dilemma

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Fig. 2
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Two-dimensional illustration of the authenticity dilemma

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Fig. 3
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Reconceptualizing the authenticity dilemma through thick authenticity

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Fig. 4
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Model of the effects of authentic learning

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Outlining the authenticity dilemma

Brown and colleagues (1989) give an illustrative example of a learning activity that is not authentically contextualized, namely vocabulary learning with the help of dictionary definitions and exemplary sentences. They argue that “learning from dictionaries, like any method that tries to teach abstract concepts independently of authentic situations, overlooks the way understanding is developed through continued, situated use” (Brown et al., 1989, p. 33). This claim is supported by research on the effectiveness of word-focused vocabulary-learning activities (e.g., word lists or fill-in-the-blanks), which can be characterized—similar to learning from dictionaries—as being not situated in authentic contexts. Findings of a meta-analysis demonstrate that word-focused activities are effective in the short term but that these positive effects sharply decline in the longer term (Webb et al., 2020). Hence, these non-authentic activities apparently do not promote a robust learning of the vocabulary. It is likely that such decontextualized and thus non-authentic activities have not only limited cognitive but also low motivational effects.

Specifically, according to Lepper (1988), contextualization, which—similar to authenticity—refers to activities that highlight the real-world functionality of the to-be-learned knowledge, constitutes a strategy for promoting learners’ intrinsic motivation to learn and master certain content. Hung et al. (2012) describe contextualization as the “central premise of authenticity” (p. 1072) and as being inherent to learning that is meaningful to learners. Also, Newmann and Wehlage (1993) describe the implementation of authenticity as a way to make the learning activities intrinsically meaningful or valuable to students. Meaningful activities and tasks are moreover seen as features of the learning setting that are likely to promote learners’ situational interest—“a state or an ongoing process during an actual learning activity” (Krapp, 2002, p. 388)—in reengaging with particular content (Hidi & Renninger, 2006). Hence, authentic learning settings try to demonstrate the meaning, value, relevance, and functionality of the target learning content to learners and appear, thereby, particularly promising for fostering motivational outcomes.

Consequently, some areas of research have focused on (solely) examining the motivational effects of authentic learning settings. For instance, numerous studies on the effectiveness of out-of-school labs have investigated how a visit of such a lab affects students’ situational interest in science (for reviews, see Priemer & Pawek, 2014; Neher-Asylbekov & Wagner, 2023). Research has demonstrated positive short-term and partly also long-term effects of out-of-school labs on students’ interest in science (Neher-Asylbekov & Wagner, 2023; Priemer & Pawek, 2014). Betz (2018) found not only a positive effect of an out-of-school lab on students’ situational interest, but also that this effect is mediated by students’ perceived authenticity of the learning setting. Findings of two other out-of-school lab studies also show that students’ perceived authenticity of the learning activity implemented in the out-of-school lab is indeed associated with their situational interest (Nachtigall & Rummel, 2021). However, the findings of these two studies also show no or only a small relation between students’ perceived authenticity and their cognitive learning outcomes (i.e., knowledge). Also, findings of a literature review of 50 (quasi-)experimental studies on the effects of authentic learning suggest that certain features of an authentic learning environment may be beneficial for motivational outcomes but at the same time less effective for cognitive learning outcomes (Nachtigall et al., 2022). Specifically, while 10 out of the 15 studies that demonstrated high motivational effects were characterized by the use of authentic materials and tools in order to reflect real-life experiences, 12 out of the 16 studies that showed small or no cognitive effects were also characterized by the use of authentic materials and tools and the goal of providing real-life experiences (Nachtigall et al., 2022). Similar findings suggesting differential effects of authenticity, or related concepts, such as contextualization, immersion, or fidelity, on motivational and cognitive learning outcomes can be detected in studies on context-based learning (Choi & Cho, 2002; Kölbach & Sumfleth, 2013; Podschuweit & Bernholt, 2018; for a review, see Bennett et al., 2007), immersive learning (Parong & Mayer, 2018, 2021; Makransky et al., 2021; for reviews, see Rosendahl & Wagner, 2023; Jensen & Konradsen, 2018), simulation-based learning (Dankbaar et al., 2016), and learning in out-of-school labs (Itzek-Greulich et al., 2017). To briefly summarize: So far, we have argued that non-authentic learning activities which are of little meaning to students, likely lead to neither cognitive nor motivational learning outcomes. Authentic learning environments may help to make learning more valuable and relevant to students, thus increasing their motivation to (re)engage with the learning content. However, as the following examples from different research fields suggest, the potentially positive effects of authenticity may remain at this motivational level.

Regarding context-based learning, the literature review of 17 studies conducted by Bennett et al. (2007) as well as the studies by Choi and Cho (2002) or Kölbach and Sumfleth (2013) shows no effects of context-based learning on learners’ achievement in or understanding of science but positive effects on their attitudes toward and interest in science. Consequently, Podschuweit and Bernholt (2018) conclude that the positive effects of context-based learning approaches on motivational outcomes have been empirically well-established, while clear evidence on the cognitive effects is lacking. With respect to immersive learning, the media-comparison studies by Parong and Mayer (2018, 2021) as well as by Makransky et al. (2021) demonstrate a positive impact of learning in immersive virtual reality environments on learners’ engagement, interest, or emotions but no or even a negative effect on their performance on a knowledge test. Consistent with the results of these exemplary studies, the literature review of 21 studies conducted by Jensen and Konradsen (2018) suggests that the use of virtual reality with head-mounted displays has positive effects on learners’ attitudes toward the immersive technology, especially their perceived usefulness for learning and perception of the immersive experience being exciting and interesting, but no clear effects on cognitive learning outcomes. The same applies to the results of the literature review of 44 studies by Rosendahl and Wagner (2023), showing positive effects of immersive 360° videos on learning motivation and student engagement but no clear evidence for positive effects on learning success. With regard to simulation-based learning, such differential effects are less apparent, as meta-analyses either focus on the cognitive effects only (for reviews, see Chernikova et al., 2020; Hegland et al., 2017) or demonstrate positive effects of simulation-based learning on both motivational and cognitive outcomes (for reviews, see Oh et al., 2015; D’Angelo et al., 2014). However, exceptions pertain to, for instance, the study by Dankbaar et al. (2016) who demonstrated that their simulation of an emergency room yielded positive effects on student engagement, but no effects on students’ knowledge of emergency care. Regarding learning in out-of-school labs, differential effects are also less apparent, as this area of research has, as mentioned above, especially focused on investigating motivational rather than cognitive effects. The findings of corresponding literature reviews suggest positive effects of out-of-school labs on motivational outcomes (see Priemer & Pawek, 2014; Neher-Asylbekov & Wagner, 2023). Studies that have solely examined the cognitive effects of out-of-school labs provide mixed evidence: Itzek-Greulich et al. (2015) found no effect of the out-of-school lab on students’ knowledge acquisition, while Scharfenberg et al. (2007) found such an effect of the learning location. A study that investigated both motivational and cognitive effects conducted by Itzek-Greulich et al. (2017) showed that learning in an out-of-school lab can have positive effects on certain motivational outcomes but no (even negative) effects on students’ achievement. Hence, in line with the results of the literature review of 50 studies on the effectiveness of authentic learning by Nachtigall et al. (2022), findings from literature reviews and exemplary studies from research areas closely related to authentic learning point to differential effects of authenticity or related concepts, such as contextualization, immersion, or fidelity. Interestingly, the explanations that have been developed for these differential effects on motivational and cognitive outcomes are relatively similar across these different areas of research.

For context-based learning, Podschuweit and Bernholt (2018) point out that critics see these attempts of contextualization as risky in terms of information overload that blurs the core scientific ideas and thus hinders learning (see p. 718). Parong and Meyer (2018, 2021) provide a similar explanation for immersive learning. They assume that the immersive features of virtual reality media—which try to trigger a sense of presence, that is, a feeling of actually being and moving in the virtual environment (Cumming & Bailenson, 2016)—promote emotional and motivational reactions but distract the user from cognitively processing the content. Based on the cognitive load theory (Sweller et al., 1998) and the cognitive theory of multimedia learning (Mayer, 2014), they describe these immersive features as extraneous materials and seductive details that cause additional cognitive processing of stimuli that are irrelevant or redundant for learning the targeted content (see Parong & Mayer, 2018, p. 9–10; Parong & Mayer, 2021, p. 1448). A similar conclusion is drawn by Dankbaar et al. (2016) for simulation-based learning. They describe that the high physical fidelity of their online simulation of an emergency room led to high engagement but probably also to confusion and overload in learners, hampering their learning (see Dankbaar et al., 2016, p. 517). For learning in out-of-school labs, Itzek-Greulich et al. (2017) assume that students perceive the “novelty space” (Orion & Hofstein, 1994) of an authentic out-of-school-lab setting as interesting and motivating, but that they need the situational circumstances of the “highly structured school” (Itzek-Greulich et al., 2017, p. 110) environment in order to learn. Hence, attempts of creating authentic learning experiences through contextualization, immersion, simulation, out-of-school locations, etc. go along with complexity and novelty which can be motivating but at the same time distracting, confusing, and overloading for learners, hampering their cognitive processing of the to-be-learned content. This may then also be the reason why the use of authentic materials and tools in order to provide real-life experiences led in previous studies on authentic learning rather to high motivational effects but to limited cognitive effects as suggested by the literature review from Nachtigall et al. (2022).

To promote cognitive processing and thus learning, cognitive load theorists emphasize the need for direct instructional guidance which refers to the provision of “information that fully explains the concepts and procedures that students are required to learn” (Kirschner et al., 2006, p. 75). There is indeed ample evidence suggesting that learning approaches with direct instructional guidance are more effective for cognitive learning outcomes than approaches with minimal or no guidance (for reviews, see e.g., Mayer, 2004; Alfieri et al., 2011). However, by adding direct instructional guidance, the complexity and thus the authenticity of the learning environment are—at least at first glance—likely to decline. The complexity of the task and the learning material as well as the self-directed and independent inquiry process of the learners while working on complex learning materials are defined as elements of authentic learning settings (e.g., Herrington & Oliver, 2000; Rule, 2006). In the context of science education, for instance, Hodson (1999) describes teacher-led approaches with direct instructional guidance that provide learners with recipes on how to solve standard problemsFootnote 1 or on how to conduct pseudo-experiments as being non-authentic as they promote the notion of scientific inquiry being nothing else than an algorithmic application of simple procedures (see p. 779 and p. 784). Also, Sommer et al. (2018) claim that the higher a learning setting is characterized by teacher-led instruction and didactic reduction, the lower is its authenticity level (see p. 254–255), as it then strongly differs from professional contexts in the real world (e.g., the work of scientists). In addition to the risk of reducing the authenticity level of the learning setting and thereby to promote inadequate conceptions about discipline-specific practices, such as scientific inquiry, the provision of direct instructional guidance is likely to inhibit effects on motivational outcomes. This assumption is supported by studies comparing the motivational effects between teacher-led and learner-centered approaches (Hänze & Berger, 2007; Carrabba & Farmer, 2018; for a meta-analysis, see Wijnia et al., 2024) as these studies demonstrate that learner-centered approaches (e.g., collaborative learning, project-based learning, problem-based learning) lead to higher motivational effects (e.g., intrinsic motivation, engagement, interest) than teacher-led approaches (e.g., direct instruction).

Against this background, it can be assumed that there exists a tension between authenticity on the one hand and instructional guidance (including structuring and didactic reduction)Footnote 2 on the other hand. Specifically, a high level of authenticity often goes along with complex learning materials and student-centered learning activities, while a high level of instructional guidance often goes along with didactically reduced, well-structured learning materials and teacher-led approaches. Authenticity and guidance each can have certain advantages as well as disadvantages for learning. An authentic learning setting with complex learning materials and learner-centered activities is likely to promote motivational outcomes but may hamper cognitive learning outcomes, while a structured and didactically reduced learning setting with direct instructional guidance is likely to promote cognitive learning outcomes but may hinder motivational outcomes. A learning setting that is neither authentically contextualized nor highly structured (such as language learning with the help of dictionaries) is likely to have neither motivational nor cognitive effects. We describe these tensions between authenticity and guidance as the “authenticity dilemma.” The question arises whether these tensions can be mitigated or whether authenticity and guidance are mutually exclusive as suggested by the one-dimensional illustration of the authenticity dilemma in Fig. 1.

Accordingly, an increase of the authenticity level of a learning setting would go along with a decrease of the degree of instructional guidance and vice versa. However, this oversimplified illustration implies four obviously incorrect claims. (1) Motivational and cognitive learning outcomes are not related to each other. This claim contradicts empirical evidence on the relations between motivational and cognitive learning outcomes (for a review, see Wigfield & Cambria, 2010). (2) The more guidance is provided, the higher are students’ immediate cognitive learning outcomes. This claim is contrary to Vygotsky’s idea of the zone of proximal development (Vygotsky, 1978). That is, even if instructional guidance is provided, students may not develop an understanding when their ability to understand the target learning concepts is outside their zone of proximal development. In addition, according to the assistance dilemma (Koedinger & Aleven, 2007), the withholding of instructional guidance may reveal under certain circumstances advantages for students’ cognitive learning outcomes. (3) The higher the authenticity and, consequently, often the complexity within a learning setting is, the higher are students’ motivational learning outcomes. However, too high complexity in learning settings can evoke frustration and a lack of perceived competence in students. According to self-determination theory, the experience of competence is fundamental for enhancing one’s intrinsic motivation (e.g., Ryan & Deci, 2000). (4) High authenticity in learning and teaching contexts does not tolerate guidance. The next section discusses this last claim.

Reconceptualizing the authenticity dilemma

The claim that high authenticity in learning and teaching contexts does not tolerate guidance is in conflict with learning approaches that can be characterized as highly authentic and at the same time include guidance, mentoring, or scaffolding. Striking examples for such approaches relate, for instance, to the cognitive apprenticeship model (Collins et al., 1991): The goal of cognitive apprenticeship is to teach the strategies that experts use to solve and handle complex problems. For this purpose, students should explore and work on realistic tasks and problems (i.e., authenticity is provided) and the teacher should promote the development of learners’ expertise by modelling, coaching, and scaffolding the problem-solving processes (i.e., guidance is provided). Thereby, it is assumed to promote both learners’ intrinsic motivation as well as their cognitive skills (Collins et al., 1991).

Productive Failure is described as a learning environment that incorporates design principles of cognitive apprenticeship (Collins & Kapur, 2014). Productive Failure combines two successive learning phases: an initial problem-solving phase that asks students to generate multiple solution ideas to a novel and complex real-world problem and an instruction phase that builds on students’ (often erroneous) solution ideas and compares and contrasts the features of the student solutions to the components of a canonical expert solution (Kapur & Bielaczyk, 2012). This approach is characterized as an authentic learning activity that emulates processes of scientific inquiry (Kapur & Toh, 2015; Nachtigall & Rummel, 2021), and it includes explicit instructional guidance. Moreover, Productive Failure and similar problem-solving prior to instruction approaches have been demonstrated to promote both cognitive learning outcomes, such as conceptual knowledge (for a review, see e.g., Loibl et al., 2017), as well as motivational outcomes, such as mastery-goal orientation (Belenky & Nokes-Malach, 2012) or interest and enjoyment (Weaver et al., 2018, Study 2).

A further approach that is closely related to the paradigm of cognitive apprenticeship refers to virtual internships. Virtual internships provide students with authentic experiences of professional discipline-specific practices, such as engineering problem-solving, in an online environment that offers individualized mentoring (Chesler et al., 2015). These online environments situate students into the role of interns at a fictitious company where they have to independently as well as collaboratively work on different tasks that simulate authentic discipline-specific problems and practices (Chesler et al., 2015). In the role of senior experts of the company, trained mentors guide the students through email, chats, and online team meetings during their internship (Chesler et al., 2015). In an evaluation study (Chesler et al., 2013) and an experimental study (Arastoopour et al., 2014), virtual internships simulating authentic engineering practices have been demonstrated to affect students’ engineering content learning (Chesler et al., 2013) as well as their engagement in the internship (Chesler et al., 2013) and motivation to persist in an engineering career (Arastoopour et al., 2014).

Other examples for learning environments that combine authenticity and guidance relate to problem-based and inquiry learning. In these approaches, learners are engaged in authentic discipline-specific practices by working on complex and ill-structured problems that emulate authentic problems faced by professionals, such as physicians or scientists (e.g., Hung et al., 2014; Pedaste et al., 2015). Although problem-based and inquiry learning activities are highly learner-centered, teachers provide facilitation (e.g., scaffolding, direct instructional guidance) during different phases of the activity (Hmelo-Silver et al., 2007). Numerous studies have shown the effectiveness of problem-based learning (for a review, see e.g., Hmelo-Silver, 2004) and inquiry learning (for a review, see e.g., Minner et al., 2010) for certain cognitive outcomes, such as flexible knowledge application. Findings of a recent narrative review on inquiry learning also emphasize the beneficial effects of inquiry-based instruction (i.e., a combination of inquiry learning and guidance) on various learning outcomes, especially on students’ deep conceptual understanding (de Jong et al., 2023). Fewer studies have focused on investigating the effectiveness of problem-based and inquiry learning approaches for students’ motivational outcomes, but there is some empirical evidence suggesting positive motivational effects of guided inquiry-based learning (Kang & Keinonen, 2018; Meulenbroeks et al., 2024) and of learning with problems (Rotgans & Schmidt, 2019; Wijnia et al., 2024). It should be noted that an analysis of PISA data points once again to the authenticity dilemma, as the results show that the higher the amount of guidance in science inquiry activities, the higher the students’ science achievement, but the lower their science attitudes (Jiang & McComas, 2015). These results should be interpreted with caution because they are based on correlational analyses and on students’ self-reports of the frequency of specific inquiry activities in their science classes.

These examples mostly demonstrate that it seems to be possible to reconceptualize the authenticity dilemma by combining authenticity with guidance in learning environments and, thereby, to promote both cognitive and motivational learning outcomes. Consequently, the tensions between authenticity and complexity on the one hand and guidance and structure on the other hand are more appropriately visualized by the two-dimensional illustration in Fig. 2 than by the one-dimensional illustration in Fig. 1.

Figure 2 summarizes the theoretical assumptions and empirical evidence discussed above. Accordingly, it is likely to assume that learning activities which are neither structured nor authentic, such as word-focused vocabulary learning activities, have limited motivational and cognitive effects. To promote motivational reactions, authentic contextualization appears highly promising, but an impact of authenticity on cognitive learning outcomes is unlikely if no or minimal guidance is provided (as in unguided discovery learning). To promote cognitive processing, guidance appears necessary, but without demonstrating the real-world relevance and functionality of the to-be-learned knowledge to learners, it is unlikely to evoke motivational effects (as in lectures with direct instruction only). Approaches that combine authenticity and guidance, such as cognitive apprenticeship approaches, have the potential to foster both motivational and cognitive learning outcomes.

Implications of the authenticity dilemma

The main implication of the authenticity dilemma suggests that the design of authentic learning settings goes along with decisions on the interplay and relation between authenticity and complexity on the one hand and guidance and structure on the other hand, and that these decisions are likely to evoke differential effects on motivational and cognitive learning outcomes. Hence, neither a high degree of authenticity nor a high degree of instructional guidance seems to be per se beneficial for learning. However, besides this main implication, the empirical evidence discussed above, which the authenticity dilemma builds on, allows further implications that relate to (1) the mode of authenticity, (2) the degree of authenticity, (3) the assessment of authentic learning, and (4) theoretical assumptions related to the effects and mechanisms of authentic learning.

Mode of authenticity

The authenticity dilemma implies that the mode of authenticity (physical vs. cognitive realism) should be considered for the design of authentic learning environments as it likely evokes differential effects on learning processes and outcomes. Specifically, the empirical evidence on the effectiveness of authentic learning discussed so far suggests that motivational and cognitive learning outcomes might be best achieved by means of “cognitive realism” (Herrington et al., 2003; Smith, 1986) rather than by physical realism of the learning environment. Based on a literature review of research on computer-based simulations, Smith (1986, 1987) concluded that the physical realism or fidelity of the stimulus materials might be less important for learning than the cognitive realism of the learning activity achieved by engaging learners in realistic decision-making and problem-solving processes (Smith, 1986, p. 651; Smith, 1987, p. 409). Herrington et al. (2003) see this assumption supported by their findings of interviews with teachers and instructional designers of web-based courses. They conclude that the cognitive realism of the task that asks learners to engage with real-world professional practices is of greater importance for learning than the physical fidelity and thus the real-life likeness of the environment (Herrington et al., 2003).Footnote 3

Looking at the explanations given by Parong and Mayer (2018, 2021), Dankbaar et al. (2016), and Itzek-Greulich et al. (2017) on why the authenticity, immersion, or fidelity of their learning settings had only motivational but no cognitive effects, their explanations all refer to external features of the learning environment (i.e., stimulus materials or location) that were probably distracting or confusing for students. Except for the study from Dankbaar et al. (2016), these studies are not related to simulation-based learning and, thus, not directly concerned with the concept of physical realism or fidelity. However, immersive virtual-reality environments like the ones in the studies by Parong and Mayer (2018, 2021) aim to promote a sense of presence and, thus, a feeling of physically being in the virtual environment. Out-of-school labs as in the study by Itzek-Greulich et al. (2017) are usually located at universities or research institutes and equipped with diverse instruments and tools such that visitors get a feeling of being at a research site. Thus, similar to digital simulations with a high physical fidelity, these learning environments try to promote a sense of actually being in a certain virtual or professional environment and, thus, a sort of physical realism.

Looking at the characteristics of approaches that usually combine authenticity with guidance (i.e., productive failure, virtual internships, inquiry learning, and problem-based learning) and that have been demonstrated to be effective for both motivational and cognitive learning outcomes, these approaches mainly focus on achieving cognitive realism by emulating complex real-world problems and real-world professional practices.

However, this does not mean that learning settings focusing on physical realism should be abandoned. Due to their potential benefits for learners’ interest, engagement, and learning motivation more generally, it appears necessary to find ways for promoting learners’ cognitive processing and outcomes when being engaged in these environments. For instance, findings from research on learning in out-of-school labs show that framing an out-of-school lab by preparatory and follow-up lessons in school can increase students’ achievement (Itzek-Greulich et al., 2017), especially the knowledge acquisition of students who are not used to visiting out-of-school labs (Reimann et al., 2020). In these two studies, the close connection between out-of-school learning and in-school learning did not weaken the positive motivational effects that emerged from solely visiting the out-of-school labs without any preparatory or follow-up work in school. For immersive learning, Parong and Mayer (2018) found that segmenting a virtual-reality lesson and asking learners to summarize each segment before watching the next one led to higher cognitive effects (i.e., performance on a knowledge test) than merely watching an unsegmented virtual-reality lesson. The segmenting and summarizing did not weaken the motivational effects of the virtual-reality experience. Hence, these examples illustrate the possibility of combining physical realism with instructional guidance and structure and, thereby, to evoke cognitive and motivational learning outcomes. Regarding simulation-based learning, the need to combine the high physical fidelity of a simulation with instructional guidance is further emphasized by reflections from learners who participated in the computer simulation tested by Dankbaar et al. (2016), who remarked that they would have preferred more feedback and additional instructional guidance. As Chernikova et al. (2020) point out, simulation-based learning environments usually include—as an inherent part of the scenario—instructional support (e.g., feedback) to a certain extent. Consequently, most of the studies included in the meta-analysis on the effects of simulation-based learning in higher education conducted by Chernikova et al. (2020) implemented instructional support (only 12% of the treatments tested in the included studies did not incorporate any instructional support). This might then be the reason why their findings suggest high cognitive effects of highly authentic simulations.

Degree of authenticity

This brings us to a further implication of the authenticity dilemma referring to the degree of authenticity. The aforementioned implication that the mode of authenticity might affect the effectiveness of authentic learning as well as the assumption that authenticity and instructional guidance should be combined in order to evoke motivational and cognitive effects suggest that full authenticity or a very high degree of authenticity is unnecessary, even undesirable, and also not feasible in learning settings.

The latter is emphasized by Sommer et al. (2018) who point out that authentic learning settings always differ from reality, because the reality is too complex for being fully mirrored in a learning setting which would be, moreover, highly overwhelming for learners (p. 255). Hence, the implementation of authenticity in learning settings can merely constitute an approximation to reality, as authentic learning settings are and need to be didactically reduced and structured to a certain extent (Sommer et al., 2018, p. 254). Thus, guidance or structure is needed to either simplify the complexity of the real-world or to provide a framework that helps learners to interpret and make sense of the complexity. In addition to or even instead of simplifying, guidance can also help to problematize complex disciplinary constructs and ways of thinking that learners might otherwise overlook during their work on a given task (Reiser, 2004, p. 287–289). From this point of view, guidance can provide learners the opportunity to engage with disciplinary ideas and practices and thus even make the learning activity authentic. Hence, achieving a high degree of authenticity by waiving any guidance, structure, or didactic reduction seems to be less advisable. With regard to simulation-based learning, Chernikova et al. (2023) even conclude—based on the findings of their meta-analysis—that authenticity on the one hand and the salience of information in the learning situation on the other hand constitute two independent design components. Increasing the salience of relevant information, e.g., through certain instructional support measures, is assumed to reduce the complexity of the learning material (Chernikova et al., 2023).

Furthermore, full or high authenticity in the sense of implementing as many design elements of an authentic learning setting as possible appears unnecessary considering learners’ personal perceptions of authenticity. Specifically, Gulikers et al. (2005) compared a highly authentic learning setting that incorporated seven out of the nine design elements of authentic learning settings described by Herrington and Oliver (2000) with a distinctly less authentic learning setting. They found that students of both conditions did not differ in their reported perceived authenticity of the learning setting. Therefore, it can be assumed that perceived “authenticity is in the eye of the beholder” (Gulikers et al., 2008; see also Barab et al., 2000), and that certain design elements are more important for learners’ perceived authenticity than others. Specifically, Gulikers et al. (2005) concluded based on their findings that the authenticity of the task, which was the same in both conditions, probably played the most central role for students’ perceptions (p. 519). This conclusion supports the importance of achieving cognitive realism in authentic learning settings. Consequently, instead of implementing authenticity to the highest degree possible, it might be more promising to focus on specific design elements, such as the tasks and practices that learners are asked to engage with.

The findings of the meta-analysis from Chernikova et al. (2020) further support (1) that a high degree of authenticity might be unnecessary for effective (simulation-based) learning and (2) that it is sufficient to focus on the authenticity of certain features of the (simulation-based) learning setting (p. 518 and p. 523). Specifically, their findings show that simulations with low authenticity—although less effective than simulations with high authenticity—still lead to high cognitive effects. Moreover, simulations that implemented one or few highly authentic features were as equally effective as simulations that tried to implement as many highly authentic features as possible. However, as this meta-analysis focused on cognitive effects, it remains unclear whether these findings also apply to motivational effects of simulations.

Assessment of authentic learning

A further implication that arises from the outlined authenticity dilemma refers to the assessment of the effectiveness of authentic learning. Hmelo-Silver et al. (2007) emphasize for inquiry learning and problem-based learning—as two examples of learning approaches that combine authenticity with guidance—that these approaches aim to promote learning outcomes that go beyond mere content learning and, thus, the acquisition of procedural and conceptual knowledge. Although inquiry learning and problem-based learning activities have been demonstrated to be effective for content learning, they appear particularly promising to foster knowledge about domain-specific epistemic practices as well as skills related to self-regulated learning, problem-solving, and reasoning (Hmelo-Silver et al., 2007, p. 102–103).

The assumption that the outcomes of authentic learning go beyond mere content learning is further supported by the epistemic frame hypothesis (e.g., Shaffer, 2006). The epistemic frame hypothesis suggests that authentic learning environments (such as virtual internships) that emulate the tasks and practices of professionals and thus allow learners to situate themselves into the role of professionals who enter and become part of a certain community of practice promote the acquisition and internalization of the grammar of that community’s culture (e.g., Chesler et al., 2015). According to Shaffer et al. (2009), this grammar or, in other words, epistemic frame of a community of practice is not only formed by the skills and shared knowledge of the community but also by the identity and beliefs of the members of that community as well as their epistemology, that is, their “ways of making decisions and justifying actions” (p. 36). Consequently, in order to investigate the effectiveness of authentic learning settings, it appears necessary to assess a variety of cognitive (e.g., skills and knowledge) and motivational (e.g., interests and beliefs) outcomes and processes (e.g., decision making), instead of focusing on the assessment of learners’ knowledge acquisition only. The likelihood of evoking differential effects through authentic learning (i.e., motivational but no cognitive effects) additionally emphasizes the need to assess a variety of learning outcomes and processes and not to solely focus on the assessment of either cognitive or motivational outcomes.

Besides the question on what to assess, examining the effects of authentic learning moreover requires to think of the question on how to assess the effectiveness of authentic learning settings. It has been argued that authentic learning should be evaluated through authentic assessments (e.g., Gulikers et al., 2004). The central idea of authentic assessments is “that the things students do when being assessed (assessment tasks) should be more like the things students do while learning (learning tasks)” (Shaffer & Resnick, 1999, p. 198). The findings of the literature review from Nachtigall et al. (2022) indeed suggest that it might be beneficial to implement authentic assessments in order to detect and prove the effects (especially the cognitive effects) of authentic learning settings. However, the findings further suggest that research (at least experimental studies) on the effectiveness of authentic learning has not yet been focused on using authentic assessments.

Contributions to a theory on authentic learning

The discussed implications of the authenticity dilemma suggest that the full potential of authentic learning settings might be best achieved through thick authenticity. In a review of 100 papers that refer to authentic learning, Shaffer and Resnick (1999) identified four different kinds of authenticity (i.e., real-world authenticity, personal authenticity, disciplinary authenticity, and assessment authenticity). They argued that all four kinds of authenticity are intertwined and thus should be combined in order to design for thickly authentic learning environments. Fougt et al. (2019) conducted an update of this review and identified a further kind of authenticity, namely teacher authenticity.

The discussions around the mode of authenticity (see implication #1) can be linked to real-world and personal authenticity. Real-world authenticity refers to learning materials and activities “that reflect or recreate some aspect of the world outside of school” (Shaffer & Resnick, 1999, p. 198). Personal authenticity refers to topics and activities that learners are personally interested in and that they find meaningful and engaging (Shaffer & Resnick, 1999). Real-world authenticity could be seen as an approach to achieve some sort of physical realism or real-life likeness of the learning setting by the use of certain stimulus materials. Thereby—according to the main implication of the authenticity dilemma—it is likely to promote motivational outcomes, such as interest, intrinsic motivation, or engagement, and, thus, personal authenticity.

The discussions around the degree of authenticity (see implication #2) can be linked to teacher authenticity and disciplinary authenticity. Teacher authenticity refers to teachers that are professionally and emotionally engaged with their students and the work of their students (Fougt et al., 2019). Disciplinary authenticity refers to learning activities that emulate professional discipline-specific practices (Shaffer & Resnick, 1999). The discussions around the degree of authenticity above emphasized the central role that teachers play for helping and guiding learners to engage with and make sense (teacher authenticity) of the complexity of the real world and especially of complex disciplinary ideas and practices (disciplinary authenticity). According to the main implication of the authenticity dilemma, the provision of guidance makes it likely to foster cognitive outcomes, such as the acquisition of knowledge.

Implication #3 suggested that the effectiveness of authentic learning settings might be best detected and proven by the measurement of a variety of learning outcomes and processes (i.e., skills, knowledge, interests, beliefs, and decision-making processes) through assessments that are aligned with the actual learning activities. Hence, the third implication is strongly linked to assessment authenticity.

Consequently, as illustrated in Fig. 3, it can be assumed that a thickly authentic learning environment is likely to evoke and demonstrate effects on learners’ motivational as well as cognitive learning outcomes.

All of these theoretical assumptions on the effectiveness of authentic learning which are based on empirical evidence of previous studies lead to the need to further develop the theoretical model of authenticity in teaching and learning contexts developed by Betz et al. (2016). This model implies that authentically designed learning settings simultaneously affect various cognitive and motivational outcomes and that all of these effects are mediated through learners’ perceived authenticity of the learning setting. Moreover, the role of guidance and structure on the one hand and the form of assessment on the other hand for detecting the effectiveness of authentic learning are not emphasized in the model. Therefore, we propose the model that is illustrated in Fig. 4. This model implies that implementing authenticity into the learning setting particularly evokes motivational effects which are mediated through learners’ perceived authenticity (light grey boxes with dashed lines). Framing this authenticity by guidance and structure makes it likely to additionally promote cognitive effects (dark grey boxes). It is reasonable to assume that these cognitive and motivational effects can be best detected through authentic assessments (box with dotted lines).

Discussing the authenticity dilemma

To probe our assumptions that both underlie and arise from the authenticity dilemma, the following sections discuss whether and how the ten papers that are included in the special issue perspectives on authentic learning support the claims that we have derived so far.

Differential effects of authenticity

Three studies in the special issue varied the authenticity of learning settings and investigated the effects of this variation on both motivational and cognitive outcomes. All three studies provide evidence for the assumption that the intended authenticity of the learning setting evokes motivational rather than cognitive learning outcomes. The study by Hagenkötter et al. (2024) found that the intended disciplinary authenticity of video models (scientists vs. peers) affected learners’ perceived authenticity of the models (favoring the scientist models). Perceived authenticity, in turn, had an effect on students’ situational interest in the content of the project, but no effect on knowledge acquisition. The study by Bichler et al. shows that the intended authenticity of the format of a simulated case (serial cue case vs. whole case) only has an effect on students’ involvement, but not on their knowledge. Their results additionally demonstrate that the perceived authenticity of the learning environment correlates only with involvement and not with performance on the knowledge test. Ries and Schwan’s (2023) study suggests that awareness of the authenticity of a historical site leads to negative emotional reactions without affecting participants’ memory performance.

Interplay between authenticity and guidance

Four other studies in the special issue investigated how guidance or certain forms of structure affect authentic learning and thus provide evidence related to our assumptions about the interplay between authenticity and guidance. The results of three of these studies are mostly consistent with our derived implications.

The results of Moser’s and Lewalter’s (2024) study show that accompanying learning in an authentic AR environment with instructional support (i.e., self-explanation strategy) enhances learners’ knowledge acquisition without affecting their perceived authenticity and satisfaction with the environment. The study conducted by Hohrath et al. (2024) also suggests—although contrary to their hypotheses—that the level of guidance does not affect learners’ perceived authenticity of a learning environment. However, given our argument developed in this paper, the provision of guidance should have led to higher knowledge gains, which was not the case in the study by Hohrath et al. (2024). One reason for this finding may be related to the fact that the form of guidance provided to the students (i.e., predetermined sequence of experiments with predetermined selection of materials) was too subtle and indirect for learning in an out-of-school lab as a novel and highly authentic learning setting, so that the complexity of the learning activity was still quite high. Güth and van Vorst’s (2024) findings suggest that the predetermination of authentic learning materials (i.e., not allowing learners to choose the authentic context of the learning task on their own)—which can be characterized as a form of guidance or structure—does not affect learners’ situational interest in or satisfaction with the content of the learning activity. Their findings further show that the absence of such pre-determination, and thus allowing learners to choose their learning tasks, can even increase their cognitive load under certain circumstances.

Only the findings of Corves et al. (2024) are not consistent with our assumptions regarding the interplay between authenticity and guidance, as they found that the provision of scaffolding during a simulation-based learning activity negatively affected learners’ perceived authenticity of the environment. However, as they did not investigate any motivational or cognitive learning outcomes, it remains unclear how the provision of scaffolding affected such outcomes. In light of our claims in this paper, one might expect that reduced perceived authenticity caused by the provision of scaffolding would only affect learners’ motivational but not their cognitive learning outcomes. Furthermore, it remains unclear whether the negative effect on perceived authenticity is due to the provision of scaffolding per se or to the timing of the scaffolding, as Corves et al. (2024) compared scaffolding during a simulation on the one hand with both no scaffolding and scaffolding after the simulation on the other hand.

Mode and degree of authenticity

Six studies in the special issue, which investigated the effects of authentic learning settings on learners’ perceived authenticity, can be related to our discussion about the mode of authenticity. Specifically, taken the findings of all six studies together, they suggest that physical realism might be more important than cognitive realism for perceived authenticity. But, as previously discussed, this might go along with beneficial effects at the motivational level but not necessarily with cognitive effects.

The study by Stürmer et al. (2024) shows that participants perceive a video simulation, which is characterized by a high degree of physical realism, as more authentic than a role play, which provides various opportunities for non-standardized interaction and thus can be characterized by a high degree of cognitive realism. Stürmer et al. (2024) do not report any findings on motivational or cognitive learning outcomes. Based on our model in Fig. 4, it might be expected that video simulation (due to its higher perceived authenticity) would lead to higher motivational effects than role play but would probably not differ from role play in terms of cognitive effects. The findings reported by Hagenkötter et al. (2024) demonstrate that students perceived video models introduced as scientists as more authentic than video models introduced as peers (which only mediated the effect on situational interest and not on knowledge). This could be interpreted as an effect of physical realism, as the look and age of the models seemingly mattered more than the things that they were doing (which was the same in both conditions). The study from Corves et al. (2024) point to a similar direction, as the medical students perceived a dialogue with patients played by trained actors in a live setting as being more authentic than a dialogue with fellow students or an interaction with virtual patients. Thus, although the task (i.e., cognitive realism) was nearly the same in all three conditions, the physical realism differed (live setting and unknown person being the patient vs. virtual setting or familiar person) and affected perceived authenticity (effects on motivational or cognitive outcomes not reported).

The findings from Bichler et al. (2024), Hohrath et al., (2024) and Moser and Lewalter (2024) all show no differences between the learning settings with respect to their impact on perceived authenticity (the studies by Bichler et al. (2024) and Moser and Lewalter (2024) further show no relation between perceived authenticity and knowledge but between perceived authenticity and motivational outcomes). A reason for these findings may relate to the fact that the physical realism between the learning settings of each study did not differ (while the cognitive realism did). In the study from Bichler et al. (2024), participants in both conditions worked in a computer-based simulation with access to the same artifacts and materials. In the study by Hohrath et al., (2024) students in both conditions conducted experiments in an out-of-school lab and used the same materials. In the study conducted by Moser and Lewalter (2024), the AR environment was the same in all three conditions. Thus, in line with our assumption, these findings emphasize that physical realism should not be abandoned when designing authentic learning environments, as it likely evokes beneficial effects on perceived authenticity and motivational outcomes although not necessarily cognitive effects. However, contrary to our discussion, the role of cognitive realism for the effectiveness of authentic learning settings appears to be less important considering these findings and, thus, requires further research.

The findings of these six studies additionally support the claim that a high degree of authenticity or, in other words, the implementation of as many design elements of an authentic learning setting as possible is probably not necessary for the following reasons: (1) the intended authenticity of a learning setting does not necessarily match what learners perceive as authentic, (2) certain elements of an authentic learning setting (e.g., elements that promote physical realism) seem to have a greater impact on learners’ perceived authenticity than others (e.g., elements that promote cognitive realism), and/or (3) as Bichler et al. (2024) hypothesize, based on their findings, there might be a threshold of authenticity, meaning that once this threshold is reached, further elements of authenticity are unlikely to have any added benefit.

Assessment of authentic learning

Most of the studies in this special issue measured perceived authenticity (7 out of 10), followed by studies that (also) assessed motivation- or emotion-related effects of authenticity (6 out of 10), and studies that (also) investigated effects of authenticity on skills and knowledge (5 out of 10). In addition to these more common variables, the studies by Nachtigall and Firstein (2023) assessed the effects of authentic learning activities on students’ epistemic beliefs about knowledge within two domains. Their findings show no such effect of authenticity, but differential correlations between learners’ perceived authenticity of the learning activity and their epistemic beliefs. Thus, in line with the epistemic frame hypothesis introduced before, it might be worthwhile to not only investigate the effects of authentic learning on knowledge, skills, and motivation, but also on learners’ beliefs and epistemology.

The discussion and synthesis of the papers in the special issue through the lens of the authenticity dilemma also suggests that studies on the effectiveness of authentic learning should not focus solely on assessing one particular outcome variable. Instead, it is necessary to consider learners’ perceived authenticity as well as motivational and cognitive learning outcomes in order to further investigate when and how authenticity affects learning.

Moving from what to assess to how to assess, the five studies in the special issue that examined the effects of authenticity on knowledge acquisition (or memory for information) did so in a more traditional and thus less authentic way, using knowledge (or memory) tests. As four of these five studies (Bichler et al., 2024, Hagenkötter et al., 2024, Hohrath et al., 2024, Ries & Schwan, 2023) found no effects of the varied levels of authenticity on knowledge acquisition, further research is needed to investigate whether the detection of cognitive effects of authentic learning settings may depend on the form, especially the authenticity, of the assessment.

Thick authenticity

The findings of Rahmian et al. (2024) could be interpreted as being in favor of thick authenticity (see Fig. 3), or at least of what Rahmian et al. (2024) call “doubly authentic” learning settings that combine two kinds of authenticity, namely personal and disciplinary authenticity. Specifically, their multiple case study shows that motivational and cognitive effects of authentic learning are most likely when the learning activities align with learners’ interests and identity (i.e., personal authenticity) as well as the domain (i.e., disciplinary authenticity), which they call the “natural configuration”. If the learning setting is only personally authentic to the learner, as their findings on the “imposed configuration” indicate, the effects are likely to remain at the motivational level.

Conclusion

Based on ample empirical evidence from various areas of research suggesting differential effects of authenticity on motivational and cognitive learning outcomes, the present conceptual paper outlines what we call the authenticity dilemma and claims that implementing authenticity into the learning setting is not per se an all-in-one solution for effective learning but is particularly beneficial for motivational learning outcomes. In light of different theoretical perspectives, such as cognitive load theory and cognitive apprenticeship, as well as empirical evidence from previous research and recent research presented in this special issue, we further claim that authenticity should be combined with instructional guidance, didactic reduction, or structure more generally to a certain extent in order to promote not only motivational but also cognitive learning outcomes. Against this background, we discussed implications related to the mode of authenticity, the degree of authenticity, the assessment of authentic learning, and a theoretical model on the conditions and effects of authentic learning. Future studies can use these implications to (1) design authentic learning settings, (2) investigate the effectiveness of authentic learning, and (3) further develop a theory on the effects and conditions of authentic learning.

The discussion of the ten papers included in this special issue through the lens of these implications showed that indeed (1) authenticity is likely to evoke differential effects, favoring motivational effects, (2) authenticity can be combined with guidance without detrimental effects on learners’ perceived authenticity and motivation, but instead partly with positive cognitive effects, (3) the mode of authenticity matters for perceived authenticity and motivational effects, (4) while the degree of authenticity does not matter, (5) it is promising and necessary to assess a variety of learning outcomes in order to uncover the effects and mechanisms of authentic learning, (6) that there is a need to develop and investigate ways to (authentically) capture the cognitive effects of authentic learning, and that (7) authenticity might be best achieved by combining different kinds of authenticity, aiming for thick authenticity. However, the discussion of the special issue papers also challenged some of the implications and thus pointed to interesting avenues for further research. That is, while we called for combining authenticity with guidance in a very general sense, the findings of the studies that investigated the role of guidance in authentic learning emphasize that the beneficial effects of guidance on authentic learning depend on the type (see Moser & Lewalter, 2024) and the timing (see Corves et al., 2024) of guidance. In addition, while we assumed that cognitive realism might be more important than physical realism for the effectiveness of authentic learning, the special issue papers rather suggest the opposite (at least in terms of effects on learners’ perceived authenticity). Thus, this conceptual paper offers evidence-based hypotheses as well as open questions about the conditions and effects of authentic learning and calls for further research to examine and address them.

Limitations

Due to our focus on motivational and cognitive effects of authenticity and especially on differential motivational and cognitive effects of authentic learning, it was not the goal and within the scope of the present conceptual paper to provide a complete review of research on the general effectiveness of authentic learning and related fields of research. Nevertheless, we largely ground our claims on both previous meta-analyses and literature reviews as well as single studies. Furthermore, we intentionally left out discussions around the personal characteristics of learners (e.g., prior knowledge) that likely have an impact on the effectiveness of not only authentic learning settings but of all learning and teaching approaches in general. In the present paper, we focused on discussing the differential effects of authentic learning from an instructional design perspective and thus how decisions related to the design of authentic learning settings may affect its cognitive and motivational learning outcomes. Nevertheless, further research (as already partly presented in this special issue) is needed to uncover how and which personal characteristics of learners affect their learning in authentic settings. Interestingly and consistent with our discussion about differential effects of authenticity, the studies in the special issue suggest that learners’ perceived authenticity is not related to their prior knowledge (see Bichler et al., 2024; Stürmer et al., 2024, Moser & Lewalter, 2024) but can be related to certain motivational prerequisites (see Stürmer et al., 2024). However, with regard to the effects of authentic learning settings on cognitive learning outcomes, research suggests that learners’ prior knowledge has an impact on their learning, for instance, in immersive environments (e.g., Han et al., 2023; Meyer et al., 2019), simulation-based settings (Chernikova et al., 2020), or out-of-school labs (Molz et al., 2022).