Augmentative and alternative communication (AAC) describes ways to support or replace spoken words for people who are unable to speak or communicate effectively using natural speech. Types of AAC include facial expressions, gestures, signs, cards with symbols, letterboards, or the use of electronic communication aids such as voice output devices [1-3]. Some published works concur that sign languages belong to AAC [4,5] while others state that they are not considered AAC [6]. The types of AAC are categorized into “unaided” and “aided,” depending on whether the patient uses solely their body to communicate or a communication aid [7]. Therefore, patients with limited expressive language may use various types of AAC to communicate.

A significant number of patients may benefit from AAC. Numerous medical conditions are known to be the cause of severe speech and language impairments that require AAC. These medical conditions are genetic disorders (eg, Down syndrome [8,9], Rett syndrome [10], and Angelman syndrome [11]), neurological impairments (eg, cerebral palsy [7,12-14], aphasia [7,15,16], dysarthria [7], apraxia of speech [7], Parkinson's disease [14]), motor neuron diseases (eg, amyotrophic lateral sclerosis and progressive muscular atrophy) [14,17,18], intellectual and developmental disabilities [19], severe hearing loss or deafness [20,21], different states of consciousness [22-24], postsurgical states affecting speech (eg, laryngectomy and tracheostomy) [7,25], and other medical conditions such as dementia [14], multiple sclerosis [14], autism spectrum disorder [7,14,26,27], visual impairment [28-30], locked-in-syndrome [31], and treatment in intensive care [32,33]. Creer et al [14] estimate that approximately 0.5% of the population of the United Kingdom are potential AAC users. Thus, AAC is a common therapeutic approach for various kinds of patients.

Patients with special communication needs, who may benefit from AAC, seek treatment in hospitals and established practices for phoniatrics and speech-language therapy [4,34]. Zinkevich et al [35] demonstrated the importance of the implementation of AAC in medical service delivery. Accordingly, phoniatricians and speech-language pathologists need to be competent in AAC. In preparation of this study, we formulated 3 main competences that phoniatricians and speech-language therapists may need in order to provide service to potential AAC users. First, phoniatricians and speech-language therapists should be able to identify patients that benefit from AAC. Second, they should be able to differentiate the types of AAC. Third, they should be able to decide which patients may benefit from what types of AAC. Thus, clinical professionals in phoniatrics and speech-language pathology (SLP) require specific knowledge in AAC to treat patients who may benefit from AAC.

What are sources of knowledge when it comes to AAC? Medical students are not obligatorily taught AAC at university. In Germany, even university students of SLP may have limited knowledge of AAC. In view of this fact, students as well as clinical professionals may search the web for information on AAC. Phoniatricians and speech-language pathologists use e-learning tools to acquire and enhance their expertise in their respective field [36,37], among other learning methods. Consequently, AAC e-learning tools are relevant in phoniatrics and SLP.

e-Learning tools can be described by their structures including formats, content areas, presentation modes, sensory modes, learning goals, target groups, and other describing structures [38]. In terms of formats, a tool can be a website, an online course, an app, a podcast, or another digital format. The learning styles of e-learning tools can be visual, auditory, or audio-visual [39,40]. Furthermore, e-learning tools have either a receptive or a performative learning goal [40]. In addition, e-learning tools may vary in content. Consequently, there are various possibilities to classify e-learning tools. This study focusses on the following e-learning structures: formats, content areas, learning styles, and learning goals.

Why is it essential to gain insight into the nature of AAC e-learning tools? Certain attributes such as “online course,” “audio-visual,” or “performative” characterize e-learning tools. This characterization may attract a specific group of learners (eg, learners who prefer a visual learning style may use websites with diagrams). A quantitative analysis could identify predominant or lacking structures. A “baseline” or status quo of the e-learning structures of AAC tools would be a starting point for further investigations, understanding and improving e-learning in AAC in the long term. However, the nature of AAC e-learning tools has yet to be investigated.

With this study, we aim to (1) explore free access AAC tools that are appropriate for e-learning in phoniatrics and SLP, (2) gain insight into the e-learning features of these tools (formats, content areas, learning styles, and learning goals), and (3) investigate structural differences within and between basic and advanced level tools. Furthermore, our goal is to establish a basis for future research in which we plan to test and evaluate a newly developed AAC e-learning tool for students of medicine and SLP.

The study of Lin and Neuschaefer-Rube [38] in 2021 was about the onset of e-learning studies that discussed the improvement of e-learning in SLP, phoniatrics, and otolaryngology. They investigated the e-learning structures of tools in SLP, phoniatrics, and otolaryngology. Differences within and between academic-level learners and clinical-professional learners were found in terms of formats, content areas, and learning goals. Thus, their study presented an initial overview of existing e-learning tools in the interdisciplinary field of SLP and phoniatrics.

Our study contributes to the improvement of e-learning in AAC as being one of the many fields of interest in phoniatrics and SLP. By systematically searching the web for AAC e-learning tools, we gain an understanding of the overall quantity of AAC tools and their availability. An analysis of the e-learning tools in AAC provides further insight into the formats, content areas, learning styles, and learning goals that were state of the art during the time of search. Accordingly, this study should add new findings to the ongoing e-learning research in phoniatrics and SLP.

Our study is an original, new, and exploratory investigation within the interdisciplinary field of medicine and SLP that targeted interactive learning tools on a niche topic. Therefore, our study does not conform to the conventional framework of a systematic review. To find the tools, a novel approach was necessary. To adhere the tenets of good scientific practice, we developed structured protocols for the following processes: systematic web-based search, tool selection, and data abstraction and validation. For transparent, complete, and accurate reporting, we proceeded in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 [41]. The checklist is provided in Multimedia Appendix 1. This study was registered at our institution Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University (no CTC-A 24‐036) and has not been registered elsewhere.

In the summer of 2023, we conducted a systematic web-based search in “Google” (Google Search), Google Scholar, PubMed, peDOCS, the Apple App Store, and the Google Play Store using company owned devices. The use of nontraditional information sources such as “Google” and Google Scholar was necessary in the search for interactive tools. We combined general, academic, medical, and educational search engines to obtain optimal results. The combination of medical and educational databases was chosen since AAC is an interdisciplinary field. By searching App store, we targeted apps that teach about AAC. Gray literature search was not conducted. We used various search terms in English (American and British) and German ranging from specific to broad in the fields of education and medicine. Our IT center ensured that the IP address would not affect the search results. The same computer was used for all web-based searches. However, random checks with a different computer were done to ensure the results were the same. Multimedia Appendix 2 shows a protocol of our search including dates of search, search engines, search terms, number of records screened, and the final tools that met the inclusion and exclusion criteria.

To ensure a clear and concise method for data abstraction, we piloted a protocol (Multimedia Appendix 3). The data abstraction was done by author JB. Automation tools were not used. Author CNR checked for validity. Both authors followed the protocol and reported no bias. Uncertainties were solved in an interdisciplinary discussion between JB as a speech-language pathologist and CNR as a medical professor.

The tools were analyzed by basic and advanced level in terms of the following e-learning structures: formats (websites, online courses, apps, and podcasts), content areas (types of AAC, diagnostics, therapy, and other content areas), learning styles (visual, auditory, and audio-visual), and learning goals (receptive and performative).

The results were analyzed using descriptive statistics. The attributes (formats, learning styles, content areas, and learning goals) of all tools were calculated, as well as inter- and intragroup differences regarding basic and advanced learner level. With respect to learning styles and content areas, overlaps were taken into consideration. The analysis was conducted using Microsoft Excel.

We identified 131 tools that met the inclusion and exclusion criteria. Multimedia Appendix 4 shows a summary of the list of tools. Of all tools, 43.5% (57/131) were basic level and 56.5% (74/131) were advanced level. Figure 1 shows the number of basic-level tools and advanced-level tools according to formats, content areas, learning styles, and learning goals. The numbers of content areas and learning styles included overlaps (ie, a tool could cover multiple content areas).

Of all 131 tools, 105 (80.2%) were websites, 21 (16%) were online courses, while only 3 (2.3%) were apps, and 2 (1.5%) were podcasts. In addition, it is worth mentioning that of the 21 online courses, only 2 (9.5%) were in German. Of the 387 content areas including overlaps, 129 (33.3%) pertained to “types of AAC,” 93 (24%) to “diagnostics,” 81 (20.9%) to “therapy,” and 84 (21.7%) to “other content areas.” Of the 265 learning styles including overlaps, “visual (text)” was predominant with 46.4% (123), followed by “visual (picture or diagram) with 37% (98). Only 12.1% (32) were “audio-visual” and only 4.5% (12) were “auditory.” For learning goals, 90.8% (119) of all 131 tools were “receptive,” while only 9.2% (12) were “performative.” Of these 12 performative tools, 4 (33.3%) pertained to “performative (directive),” 4 (33.3%) to “performative (guided discovery),” and 4 (33.3%) to “performative (directive and guided discovery).” Consequently, most of the tools were websites, taught about the types of AAC, showed the “visual (text)” learning style, and had a receptive learning goal. Multimedia Appendix 5 illustrates the distribution of formats, content areas, learning styles, and learning goals across all tools.

We investigated between-learner level differences in formats, content areas, learning styles, and learning goals. To compare basic and advanced level, the higher number of advanced level tools (respectively smaller number of basic level tools) had to be taken into account (57 basic and 74 advanced). Therefore, the absolute numbers cannot be compared. Instead, the percentages (of each component, eg, “website”) within learner levels were compared. In the following paragraphs, the results are presented as “basic level versus advanced level" when results for both learner levels appear in parentheses.

Our study set out to explore e-learning tools in AAC for phoniatrics and SLP, analyze their e-learning features and investigate differences in basic and advanced learner level. We identified 131 free access e-learning AAC tools and gained insight into their formats, content areas, learning styles, and learning goals. Most of the tools were websites, while apps and podcasts were rare. The predominant content area was “types of AAC” and “visual (text)” was the most common learning style. Most of the tools were “receptive.” Within both learner levels, “website” was the predominant format. Within basic level, none of the tools were podcasts and the predominant content was “types of AAC.” Within advanced level, the content areas were almost evenly distributed. “Visual (text)” was the predominant learning style in both learner levels. Most of the advanced tools and all basic level tools were “receptive.” Websites were more common in basic level, whereas online courses, apps, and podcasts appeared more in advanced level. The content of advanced level tools was more diagnostic-related and therapy-related. “Visual (picture or diagram),” “auditory,” and “audio-visual” learning styles were more common in advanced level. All basic-level tools and the majority of the advanced-level tools were receptive.

This study appears to be the first to investigate the structures of AAC e-learning tools in English and German. In designing our study, we were inspired by Lin and Neuschaefer-Rube [38] who analyzed e-learning tools for SLP, phoniatrics, and otolaryngology by their e-learning structures. Although they set slightly different criteria, their overall idea of investigating e-learning tools can be compared with our study. In their study, for example, learner levels were classified into “academic level” and “clinical professional level.” We chose a different classification for our study, since it cannot be assumed that all clinical professionals have previous knowledge in AAC. Most of our results reflect those of Lin and Neuschaefer-Rube [38] who also found that visual tools were predominant as well as most of the tools were receptive. The difference between the studies was, interestingly, that performative tools pertained more to academic level learners.

The comparison of our findings to yet another study turned out to be challenging. e-Learning studies increased since the COVID-19 pandemic resulting in numerous available research papers. However, other works in the fields of otolaryngology and SLP focused on surveys [45,46], the effectiveness of e-learning [47] or specific online programs [48,49], rather than exploring and analyzing the attributes of e-learning tools. Therefore, our study is a specific examination in the overall bewildering and evolving field of e-learning research.

The findings of this study must be seen in light of 5 limitations. These are (1) incompleteness and difficult replication, (2) limited search terms and search engines, (3) strict inclusion criteria, (4) binary learner level categorization, and (5) quantitative assessment rather than quality rating.

First, e-learning tools are not static since the World Wide Web is an ongoing field of updates and changes [50]. Therefore, this study is only a snapshot of the available tools at the time of data collection undergoing certain inclusion and exclusion criteria. In addition, different IP addresses may lead to different search results. Accordingly, this study is not an investigation of all existing e-learning tools in AAC nor is it replicable. Nevertheless, with a total number of 131 tools, we managed to investigate a high amount, which appears to be a fair representation of the present AAC e-learning scope.

Second, in terms of our web-based search, we could have used more search engines to possibly find more tools [51]. Likewise, we could have added more search terms. Nonetheless, our chosen search engines and search terms led to many results that would not have been manageable without strict inclusion criteria.

Third, the inclusion criterium of a mandatory definition of AAC may have eliminated some advanced tools. In fact, the authors cannot recollect that this was the case during the process of finding the tools. Nevertheless, if we had included tools that did not provide a definition of AAC, probably almost every website within the search results would have entered our study.

Fourth, the binary categorization into either “basic level” or “advanced level” did not leave room for learners with knowledge in between those levels (eg, intermediate level). However, we think that this categorization allows students and clinical professionals to choose a tool according to their knowledge level rather than to their professional status.

Finally, this quantitative study cannot give quality evaluations of the 131 tools, nor do we have proof that the authors of the tools were professionals or well versed in AAC. However, since we investigated the content of the tools, we can conclude that all tools seemed to provide correct information. Despite of its limitations, our study certainly adds to the understanding of e-learning in AAC.

Our study lays the groundwork for future research in e-learning in AAC. More work needs to be done to gain further insight into e-learning in AAC, such as (1) expanding the analysis to other formats, (2) assessing the quality of the tools, (3) conducting a survey study on e-learning in AAC, and (4) developing new AAC e-learning tools.

First, the analysis of AAC e-learning tools on video sharing platforms, social networks, blogs, and forums would be interesting, since we excluded them in our study. Furthermore, an explicit search for a particular format (eg, podcasts) could lead to more results that might be worth investigating. In addition, an assessment of misinformation on AAC in social media would be informative.

Second, a qualitative analysis of AAC e-learning tools would be beneficial for the users of these tools. The best rated tools could be added to a possible future toolbox app [37], or to a German “AAC online learning platform” suggested by Burgio [43]. Furthermore, it would be interesting to investigate correlations between the e-learning structures and the quality of the tools.

Third, a survey study in phoniatrics and SLP could help identify the preferred structures of possible AAC e-learning tools. It would be worth investigating whether phoniatricians and speech-language pathologists have the same demands and would therefore benefit from the same tool.

Finally, we suggest practical applications for the development of new e-learning tools in AAC for students and professionals in phoniatrics and SLP. One example of a possible AAC e-learning tool could be a German, advanced level, audio-visual online course with a knowledge quiz that covers all content areas relevant to phoniatricians and speech-language pathologists. On the basis of this study, we developed such an online course which is currently being tested with students of medicine and SLP at RWTH Aachen University. Another example would be the development of apps that teach AAC (in both languages).

To the best of our knowledge, our exploratory study marks the beginning of the investigation of e-learning tools in AAC for phoniatrics and SLP. We found a fair number of tools and gained insight into their formats, content areas, learning styles, and learning goals. Our data indicate that e-learning in AAC is a topic of interest and needs to be further investigated. Overall, we established a basis for future research and suggested practical applications for e-learning in AAC.